ABSTRACT

Global analogues are widely used across the exploration and production (E&P) life cycle. Analogues, used in conjunction with primary data, expand the knowledge of both the individual and team and develop insights that are not possible from using either local data or individual experience in isolation. Difficulties in the application of analogues arise when the analogues are not selected consistently, are too specific, or are in conflict with empirical local data. Most of these difficulties arise from the lack of a proper definition of analogues, absence of a systematic method of analogue selection, and poorly defined objectives for the use of analogues. Analogues are herein defined as comparable fields and reservoirs relevant to a specific question or set of questions. To select appropriate analogues, practitioners should focus on specific individual question(s) instead of “look-alike” fields.

A consistent global field and reservoir knowledge base with standardized and classified geological and engineering parameters form the basis for analogue selection and analysis. The ability to standardize knowledge on practitioners’ own E&P assets and conduct benchmark comparisons against applicable global analogues is critical to the identification of potential problems, mitigation strategies, and best practices. Appropriate application of global analogues to a local situation not only fosters creative thinking but also provides a way to quickly learn, increase confidence, and efficiently reduce risk for E&P decision-making.

INTRODUCTION

In the upstream industry, there is widespread agreement about the need and usefulness of analogue data (Sun and Wan, 2002; Howell et al., 2014), but beyond the process of selecting analogue data for proved reserves reporting (Hodgin and Harrell, 2006; Sidle and Lee, 2010), there are few well-published processes or workflows to locate and extract meaningful analogue data, either from public literature or proprietary company files. Part of the challenge facing the widespread use of analogues is that (1) data are rarely harmonized or consistently structured and (2) there is a lack of consistent methods used to filter the analogue data prior to analysis.

The filtering of sufficiently structured and harmonized data prior to analysis constitutes the first of two necessary steps in the process of using analogues. This allows the user to broadly define the scope of the analogue search using well-agreed parameters (e.g., lithology, hydrocarbon type, drive mechanism, etc.). The risk inherent to this approach is that the user may adopt overspecific filters that inadvertently restrict valid scenarios for consideration (Volpi et al., 2003). To address this issue, several attempts have been made to broaden the analogue search using a case-based reasoning approach (Bhushan and Hopkinson, 2002; Temizel and Dursun, 2013), multivariate statistical techniques (Volpi et al., 2003; Rodriguez et al., 2013), and machine learning algorithms (Brazil et al., 2018). However, relying upon algorithms to conduct screening based upon chosen key parameters and weighting factors may inadvertently include analogues that are not necessarily valid. Irrespective of the approach chosen, this study argues that the analogue selection process is best accomplished using a team-based approach, with geoscientists and engineers working toward a common goal based upon specifically chosen, standardized geological and engineering parameters.

The purpose of this paper is to present a systematic, objective, and integrated methodology for an analogue selection and solution workflow. Empirical exploration, development, and production examples are discussed within the framework of the analogue solution workflow. These examples demonstrate how risk and uncertainty can be reduced with the appropriate use of global analogue intelligence, ultimately improving decision quality and realizing value from the adoption of the workflow.

VALUE OF APPROPRIATE ANALOGUES AND COMMON PITFALLS

When properly chosen analogues are used in conjunction with primary data, they can quickly broaden and deepen the knowledge of both the individual and team through the development of new insights that might otherwise not be available from either local data alone or any individual’s prior experiences. In addition to augmenting an individual’s background and perspective, properly chosen analogues help to calibrate risk and uncertainty and can increase decision quality through all phases of the exploration and production (E&P) life cycle (Figure 1).

Figure 1.

Value of global analogues in the context of petroleum resource management framework. C = contingent resources; EOR = enhanced oil recovery; EUR = estimated ultimate recovery; P = reserves; U = prospective resources.

Figure 1.

Value of global analogues in the context of petroleum resource management framework. C = contingent resources; EOR = enhanced oil recovery; EUR = estimated ultimate recovery; P = reserves; U = prospective resources.

Analogue data are most commonly used in the case of resource estimation, particularly in the exploration and early development stages when information from direct measurement is limited (Society of Petroleum Engineers, 2018). Analogues are also commonly applied to aid assessment of economic producibility, production decline characteristics, drainage area, and recovery factor (for primary, secondary, and tertiary methods). When properly selected, analogues provide a basis for probabilistic distribution of key parameters and solutions to critical issues facing prospect evaluation, field development planning, production enhancement, and reserves booking (Figure 1).

When applying learnings derived from the use of analogues, it is important to consider that only information from appropriate analogues is useful, and poorly selected analogues can act to limit the understanding of a prospect or asset as much as a proper application can help. When analogues to the target prospect, discovery, or field are poorly understood (and therefore selected inappropriately), too narrow in scope, or dogmatically applied, they run the risk of being overly prescriptive, ignoring local variations in rock and fluid properties. Therefore, it is important to know how and when to select the right analogues and for what purpose. These sorts of challenges arise when selection is made without a structured, standardized, and classified knowledge framework or when they are chosen too specifically or arbitrarily.

When analogues conflict with local data, there is a natural tendency for users to compromise the analogue selection in a way that confirms their a priori assumption. Analogue information is commonly incorporated on an ad hoc basis, relying on a project team’s recommendation and knowledge from their own experience. Selecting the wrong population of analogues from which best practices and key learnings can be drawn is a common mistake. Like-for-like comparison is therefore essential, and it is demonstrated in this study that this is only achievable through rigorous standardization, consistent rules, and a comprehensive classification scheme.

ANALOGUE METHODOLOGY

Historically, prior to the widespread use of personal computing, the use of analogues relied upon personal experience, both individual and team-based, and analogues were commonly selected based on geographic proximity, using data available from the same play or basin. A systematic way of comparing global analogues did not exist and, where it was at all possible, the process was slow and comparisons were commonly qualitative in nature. In competitive situations (e.g., bid rounds, tenders, farm-ins, and sales), abundant, high-quality, and relevant literature on applicable analogues was challenging to find and apply within a limited time.

Today’s environment, with abundant global data available, presents a new challenge: the management, storage, and analysis of a rapidly growing, large, and diverse database. In broad terms, it can be said that the oil and gas industry has moved from a position of not having enough subsurface information to an overabundance of both data and information (Perrons and Jensen, 2015). Organizations are now more likely to be constrained by time, capability, or capital than by data. This situation tends to lead to less knowledge and decreased insight.

The data used in this study are a proprietary compilation of more than 1600 global reservoirs, compiled using more than 50,000 public domain publications. Crucially, the information used to compile this analogue knowledge base has been consistently standardized and parameterized into approximately 420 variables for each reservoir. This allows consistent and appropriate comparisons to be made on an equal basis between analogues.

Analogue Standardization

When building an analogue knowledge base from diverse public domain data sources or from internal company data, the data set rarely appears complete, and individual parameter values rarely make sense without sufficient context. Furthermore, parameter values from different sources may conflict with each other for numerous reasons (e.g., differing languages, practices, geologic terms, and engineering units). Rather than attempting numerical or statistical means to address these inconsistencies, the workflow presented here advocates structuring and standardizing the analogue knowledge itself. This process involves collecting, reviewing, and synthesizing geological, reservoir engineering, and production data on a representative sample of global reservoirs and fields. These field case studies account for more than 70% of global conventional recoverable reserves and, collectively, document both best practices and technical failures from global exploration and production over the past century. To be able to compare fields and reservoirs globally, it is first necessary to standardize terminology and units and then apply the same classification scheme to the reservoirs and fields described in the literature. Each field case study details how and why the prospect was drilled and then covers the basin genesis and source rock, followed by a detailed description of the structure and trap definition, reservoir characteristics, and fluid properties. It also covers resources and methods of hydrocarbon recovery, including development strategy, reservoir management practices, and improved recovery techniques applied and their outcomes.

A comprehensive data model with 420 geological and reservoir engineering parameters is created at both the reservoir and field level (Table 1). Each attribute is consistently defined and contains a set of standardized values following a holistic classification scheme. As examples, the hierarchy of classification for lacustrine depositional systems and environments (Figure 2) and erosional truncation traps (Figure 3) are provided. The intent of this data model is that it generates a coherent and consistent knowledge base of geological, engineering, and production parameters (Table 2). Although no classification scheme is perfect and universally agreed upon, it is critical that the terminology and parameters are consistent, well defined, and genetically based and have definitions readily accessible to the practitioners. By employing these consistent standards, practitioners can capture their own reservoir and field knowledge for comparison with a broader knowledge base.

Table 1.

Field and Reservoir Knowledge Structure with Emphasis on Parameters for Reservoir Characteristics (This Is an Example of a Greater Number of Standardized Variables)

graphic

graphic

Figure 2.

Hierarchy of knowledge classification with an example for lacustrine depositional system and environment.

Figure 2.

Hierarchy of knowledge classification with an example for lacustrine depositional system and environment.

Figure 3.

Hierarchy of knowledge classification with an example for erosional truncation traps.

Figure 3.

Hierarchy of knowledge classification with an example for erosional truncation traps.

Table 2.

Knowledge Standardization of Important Parameters for the Captain Field, United Kingdom, Central North Sea

Parameter CategoryStandardized Value
1. Field
 Field nameCaptain
 CountryUnited Kingdom
 Basin aliasNorth Sea Central
 Onshore or offshoreOffshore
 Water depth, ft344
2. General
 Reservoir unitValhall (Captain Sandstone)
 Hydrocarbon typeOil with gas
 Current statusSecondary recovery
 Reservoir temperature, °F87
 Original reservoir pressure, psi1340
 Pressure gradient, psi/ft0.45
 Drive mechanismsStrong aquifer
3. Well
 Total producers54
 Total injectors10
 Well typeExtended-reach well, horizontal well, multilateral well
 Well spacing (current), ac90
 Well EUR, thousand bbl of oil6100
4. Trap
 Tectonic settingPostrift sag
 Trapping mechanismBuried-paleorelief compaction anticline, lateral depositional pinch-out
 Seismic anomalyNone
 Structural compartment count2
 Depth to top of reservoir, ft TVDML2254
 Trap flank dip (average), °3
 Original productive area, ac9400
 Oil column height, ft269
5. Reservoir
 Reservoir ageEarly Cretaceous
 Tectonic settingPostrift sag
 Depositional environmentSubmarine fan channel
 Reservoir thickness gross (average), ft280
 Reservoir thickness net (average), ft266
 Net-to-gross ratio (average)0.95
 Net pay (average), ft246
 Reservoir lithologySandstone
 Porosity matrix (average), %31
 Permeability air (average), md7000
 Vertical to horizontal permeability ratio (average)0.2
 Permeability contrast (average)10
6. Fluid
 API gravity (average), ° API20
 Viscosity (average), cP88
 Mobility index (average), md/cP80
 Flowability (average), md·ft/cP19,573
 Original oil saturation (average), %84
 Formation volume factor oil (average), RB/STB1.05
 Bubble point pressure (average), psi1270
 Initial GOR (average), SCF/STB130
 Initial water saturation (average), %16
7. Resource
 Original oil in place, million bbl of oil1000
 Resource density oil, thousand bbl of oil/ac106
 EUR oil, million bbl of oil340
 Estimated ultimate recovery factor, %34
8. Improved recovery
 Secondary recovery methodsContinuous water injection
 EOR methodsPolymer flood
 Reservoir management practices (drilling)Horizontal well, infill drilling, step-out drilling, sidetracking,extended-reach well, multilateral well
 Reservoir management practices (sand control)Stand-alone sand screen, open-hole gravel pack, prepackedsand screen
 Reservoir management practices (artificial lift)Electric submersible pump
Parameter CategoryStandardized Value
1. Field
 Field nameCaptain
 CountryUnited Kingdom
 Basin aliasNorth Sea Central
 Onshore or offshoreOffshore
 Water depth, ft344
2. General
 Reservoir unitValhall (Captain Sandstone)
 Hydrocarbon typeOil with gas
 Current statusSecondary recovery
 Reservoir temperature, °F87
 Original reservoir pressure, psi1340
 Pressure gradient, psi/ft0.45
 Drive mechanismsStrong aquifer
3. Well
 Total producers54
 Total injectors10
 Well typeExtended-reach well, horizontal well, multilateral well
 Well spacing (current), ac90
 Well EUR, thousand bbl of oil6100
4. Trap
 Tectonic settingPostrift sag
 Trapping mechanismBuried-paleorelief compaction anticline, lateral depositional pinch-out
 Seismic anomalyNone
 Structural compartment count2
 Depth to top of reservoir, ft TVDML2254
 Trap flank dip (average), °3
 Original productive area, ac9400
 Oil column height, ft269
5. Reservoir
 Reservoir ageEarly Cretaceous
 Tectonic settingPostrift sag
 Depositional environmentSubmarine fan channel
 Reservoir thickness gross (average), ft280
 Reservoir thickness net (average), ft266
 Net-to-gross ratio (average)0.95
 Net pay (average), ft246
 Reservoir lithologySandstone
 Porosity matrix (average), %31
 Permeability air (average), md7000
 Vertical to horizontal permeability ratio (average)0.2
 Permeability contrast (average)10
6. Fluid
 API gravity (average), ° API20
 Viscosity (average), cP88
 Mobility index (average), md/cP80
 Flowability (average), md·ft/cP19,573
 Original oil saturation (average), %84
 Formation volume factor oil (average), RB/STB1.05
 Bubble point pressure (average), psi1270
 Initial GOR (average), SCF/STB130
 Initial water saturation (average), %16
7. Resource
 Original oil in place, million bbl of oil1000
 Resource density oil, thousand bbl of oil/ac106
 EUR oil, million bbl of oil340
 Estimated ultimate recovery factor, %34
8. Improved recovery
 Secondary recovery methodsContinuous water injection
 EOR methodsPolymer flood
 Reservoir management practices (drilling)Horizontal well, infill drilling, step-out drilling, sidetracking,extended-reach well, multilateral well
 Reservoir management practices (sand control)Stand-alone sand screen, open-hole gravel pack, prepackedsand screen
 Reservoir management practices (artificial lift)Electric submersible pump

Abbreviations: EUR = estimated ultimate recovery; GOR = gas–oil ratio; RB = reservoir barrel; SCF = standard cubic feet; STB = stock tank barrel; TVDML = true vertical depth below mudline.

Analogue Selection

Finding the most relevant analogues to a given prospect or field is essential to transform knowledge into critical insight and intelligence for E&P decision-making. It is a common mistake to only consider local analogues or analogue knowledge gained merely from the team’s own experiences. It takes time and skill to research each global analogue, synthesize the information, and make results available for analysis. Some “analogue work” may be as simple as creating awareness and learning about the nature of accumulations within a basin or play. When used by geoscientists new to a basin, analogues can provide an accelerated path to a global perspective: the geoscientist might ask, “What kind of traps have worked in foreland basins around the world?” Knowing what one is looking for or having an awareness of what has worked in similar settings in other areas may increase the likelihood of finding a fresh insight in a different region. Although the effective use of local data is important, there may be specific plays that are outside the knowledge of local experts. The ability to apply a globally based, structured, and classified knowledge framework to a local situation can help add an additional dimension of creativity and confidence to exploration. Examples are given herein as guidelines for analogue selection workflows to address typical challenges common to exploration, development, and production phases of the E&P life cycle.

The effectiveness of analogue application depends on an appropriate definition of an analogue, a systematic method of analogue selection, and a well-defined objective for the use of analogues. Analogues are herein defined as comparable fields and reservoirs relevant to a specific question or set of questions (e.g., analogues to understand porosity distribution and permeability anisotropy for karstic carbonates).

To select appropriate analogues, practitioners should focus on specific individual question(s) instead of “look-alike” fields. Selection of the relevant and applicable analogues depends on which part of the E&P life cycle a practitioner is concerned with, what drives project value, what challenging issues need to be solved, what critical decisions have to be made, and which information is missing. A detailed understanding of analogues is required within a structured and classified knowledge framework to ensure that valuable and real insights are captured. Methods of analogue selection and their application differ fundamentally depending on the discipline of the practitioners and the problem being addressed (Table 3). Every time the practitioners select an analogue search filter, they must question how critical and relevant the parameter in question is for the issues to be resolved, rather than make a superficial comparison to the field of interest. Problems of differing nature and for different objectives require different sets of analogues. Geoscientists may use analogues to validate play concepts, calibrate prospect uncertainty, or characterize permissible alternatives of a geologic model. When searching for analogues for this purpose, they may focus mainly on geologic parameters, such as tectonic setting, lithology or depositional environment, geologic age, trapping mechanism, sand-body type, diagenetic reservoir type, and net-to-gross ratio (Table 3).

Table 3.

Analogue Search Best Practices Illustrating Problems of Differing Nature and for Different Objectives Require Different Sets of Analogues

graphic

graphic

In contrast, reservoir engineers might use analogues to validate development concepts, better understand producibility, or estimate recovery factor. Key parameters for analogue selection in this case might include hydrocarbon type, development situation (onshore versus offshore), lithology or depositional environment, drive mechanism, rock and fluid properties, and field size (Table 3). Production engineers do not look for analogues per se; instead, they are more interested in knowledge and best practices from analogous reservoirs that share a common hydrocarbon type with similar rock and fluid properties, reservoir conditions, and production challenges. Following our analogue selection workflow, practitioners can find a range of analogues with different outcomes. These range from low-side outcomes with poor practice to high-side outcomes with best practice. Reservoir performance and recovery factor benchmarking is a fundamental step to identify the underperforming fields and apply best practices of the top-tier performers to optimize production performance and recovery efficiency (Table 3).

Analogue search and analysis is a process of research and discovery, integrating practitioners’ expertise and knowledge on their target assets with global analogue intelligence. It is critical to strike an appropriate balance between the number and relevance of analogues. The selection of analogues should make both genetic and statistical sense; probabilistic results should be conducted based upon the suite of genetically related global analogues rather than just local data. It is the authors’ experience that the most common pitfall in this process is too narrow parameter definition and overly aggressive filtering for analogues that resemble their fields in every aspect (tight categorization). Invariably, comparisons are difficult to make since there are never one-to-one matches in fields or reservoirs. To avoid this pitfall, users should look for analogues that can address a specific issue, within a structured and classified knowledge framework, instead of seeking a unique analogue to their target field. One set of appropriate analogues should only be used to calibrate one particular subsurface uncertainty.

Practitioners should start with a broad set of parameters to find a wide range of analogues (loose categorization), then narrow the field as appropriate to focus on the specific issue. No presumption or a priori knowledge is required for what is important; the only requirement is that the practitioners are open-minded and specific about their interest. Following the workflow will then indicate what is important. The general idea in the application of analogues should be to expand users’ knowledge base to the point where they can make globally informed decisions about their own local data.

Analogue Solution Workflow

Analogues are widely used to calibrate subsurface uncertainty and production performance throughout the E&P life cycle (Sun and Wan, 2002) and have been demonstrated as critical to accurate resource assessment and reserves booking (Society of Petroleum Engineers, 2018). To assist geoscientists, reservoir engineers, and portfolio managers in efficiently expanding their knowledge, as well as gaining new insights on their own prospects and assets, we propose a five-step analogue solution workflow:

  1. Define problems and objectives. Be clear about the specific problems to address and the critical questions to be answered.

  2. Consistently document knowledge. Catalog your prospects, undeveloped discoveries, and producing assets using rigorous standards, consistent rules, and a comprehensive classification scheme.

  3. Choose relevant analogues. Focus on addressing issues that are critical to an impending decision rather than “look-alike” or geographically close analogues.

  4. Benchmark targets or characterize analogues. Place the prospect or asset in question in context of the probabilistic distribution of parameter values for the selected set of analogues to discover critical issues and reveal value creation opportunities.

  5. Identify best practices. Analyze specific geological, engineering, and production parameters relevant to the critical issues identified and scrutinize potential solutions from best-in-class analogues (analogues that have a relatively high recovery efficiency for comparable rock and fluid properties, reservoir heterogeneity, and drive mechanism).

This workflow is demonstrated here using three case studies from across the E&P life cycle.

EXPLORATION APPLICATIONS

Subsurface geological analogues should form an integral part of the prospect maturation workflow to help reduce exploration uncertainty. Although individual plays and prospects are never identical, key learnings can be transferred both within and between basins, and therein lies the power of analogues. Commercially successful analogue fields can be used to both develop new ideas in mature basins and in the application of established ideas to frontier basins. “Discovery thinking” requires an open mind supported by detailed global knowledge of what is possible and what is proven.

When a prospect is being matured, geoscientists need to have confidence in the various technical parameters, which typically provide the basis for a drilling proposal. A common question decision-makers pose is, “Where are the successful analogues and how does this prospect compare to these analogues?” Benchmarking of prospects against commercially successful analogue fields allows inherent risks to be identified and managed and potential resource to be predicted with a higher degree of confidence. With the ability to search trends across similar plays, geoscientists can test if their play concepts are valid and calibrate the uncertainty ranges for various aspects of their geological model.

Misuse of Jubilee as an Analogue

Since the discovery of the Jubilee field in deep-water offshore Ghana in 2007, the industry has drilled numerous dry wells in the western Africa transform margin region. Several noncommercial discoveries have been made at the cost of hundreds of millions of dollars. All too often, those using Jubilee as an analogue assumed that Jubilee was a relatively simple upslope depositional pinchout trap supported by a brightening of amplitudes within the assumed container (Jewell, 2011). However, the single most important element in exploring for pure stratigraphic traps is the correct identification and quantification of seal (Dolson et al., 2018). An imperfect seal will change a very promising amplitude-based prospect into a dry well or a subcommercial discovery.

Generally, in stratigraphic traps, the overall size of the accumulation is limited by the column height capacity versus structural dip (Dolson et al., 2018). In most cases, this means that the steeper the structural dip, the more limited the trap size becomes. To understand the true nature of hydrocarbon entrapment for the Jubilee field, analogue selection is focused on rift and passive margin settings for (1) pure lateral pinchout trap and (2) normal-fault trap. Figure 4A shows trap flank dip versus productive area for 18 pure lateral pinchout traps in rift and passive margin settings. Jubilee, with a productive area of 19,244 ac and trap flank dip exceeding 5°, lies outside the maximum productive area limit for a given trap flank dip. In fact, all seven analogous reservoirs with trap flank dip exceeding 3° have a productive area less than 9000 ac, indicating Jubilee is less likely to be a pure lateral pinchout trap (being at the extreme of the distribution). In contrast, when benchmarking Jubilee against 226 normal-fault traps in rift and passive margin settings, it lies within the normal range of probabilistic distribution (Figure 4B), indicating Jubilee is more likely to be a combination trap with upslope faulting being an important factor in trap formation. Dailly et al. (2012) noted that the Turonian reservoirs appear to be trapped against a downthrown fault toward the northeast. Faulting seems to have been an important factor in trapping hydrocarbons for many lateral depositional pinchout traps in passive margin and rift settings and for upslope turbidite reservoirs (Amy, 2019).

Figure 4.

Trap flank dip versus productive area for hydrocarbon accumulation in rift and passive margin settings: (A) pure lateral depositional pinchout trap and (B) normal-fault trap.

Figure 4.

Trap flank dip versus productive area for hydrocarbon accumulation in rift and passive margin settings: (A) pure lateral depositional pinchout trap and (B) normal-fault trap.

As previously discussed, it is important to consider that only information from appropriate analogues is useful and that information from inappropriate analogues can be misleading. The misuse of Jubilee as an analogue for pure upslope depositional pinchout traps emphasizes the dangers of a misleading comparison. Along the western Africa transform margin, the assumption that Jubilee was a pure stratigraphic trap combined with the lack of recognition of the definitive updip fault seal led to many of the dry holes and subcommercial discoveries such as Narina-1 (2012), Mesurado-1 (2016), Fatala-1 (2017), and Ayame-1X (2017).

DEVELOPMENT APPLICATIONS

During appraisal and early field development, there are many uncertainties regarding the geologic model, number of wells needed to efficiently produce from a reservoir, well placement, pressure maintenance, recovery efficiency, and potential reservoir management programs. Comparing new discoveries with analogous producing reservoirs helps better estimate recoverable reserves and future production performance and therefore optimizes recovery methods.

When using analogues for assessing field development scenarios, detailed knowledge on reservoir heterogeneity and connectivity, well type, spacing and rate, estimated ultimate recovery (EUR) per well, rock and fluid properties, drive mechanism, recovery methods, and field size is required. Benchmarking undeveloped discoveries using these variables can identify best practices to help plan an optimal hydrocarbon recovery strategy and give greater confidence in estimating production rates and recovery factors.

Development Concept for Zama Discovery

Talos Energy and partners Premier Oil and Sierra Oil and Gas announced a world-class oil discovery at the Zama prospect, offshore Mexico. The Zama discovery was made in upper Miocene submarine fan sandstone reservoirs with a three-way dip structure sealed against a salt ridge (Offshore Technology, 2017). The field’s estimated stock tank oil initially in place (STOIIP) ranges from 1.4 to 2 billion BOE, and it has estimated recoverable reserves of 400–800 million bbl of oil (Offshore Energy Today, 2019). Analogues for the Zama discovery were used to provide benchmarks for recovery factor and development concepts with the aim to increase the ultimate recovery. In light of the previously described workflow, the following four steps were taken to analyze the Zama discovery.

  1. Defining problems and objectives: What is the recovery potential of the Zama discovery? How many wells are needed to efficiently produce from the reservoir? What is the possible reservoir management program to consider?

  2. Capturing knowledge: Based on press releases from the operator, both the text and numeric parameters have been standardized and classified using consistent rules and a holistic classification scheme (Table 4).

  3. Analogue selection: In light of the principal objective to understand recovery potential and evaluate development scenarios, the recommended analogue selection focuses on hydrocarbon type (oil), development situation (offshore), depositional environment (submarine fan), API gravity (>22°), air permeability (>100 md), and original oil in place (>500 million bbl of oil) as the critical search parameters. Twenty-two applicable global analogues are identified using search criteria relevant to the Zama development concept (Table 5).

  4. Analysis and insights: Using this method, several insights can quickly be developed from the probabilistic distribution of analogue data, including well spacing, initial well rate, EUR per well, plateau annual recovery, and recovery factor (Table 6). Applicable global reservoir analogues show that the mean recovery factor achieved is 42%, but the upper range exceeds 60% (Table 5). Higher recovery factor tends to be associated with good air permeability, relatively thick net pay, and lower viscosity (Table 5). For those fields that have exceeded 50% ultimate recovery (e.g., Buzzard, Forties, Magnus, Miller, and Nelson fields, United Kingdom, and Namorado field, Brazil), continuous water injection and conformance improvement techniques, such as water plugging, modifying injection pattern, and profile modification, have proved to be effective in optimizing the recovery (Table 7). Benchmarking of recovery efficiency against empirical recovery chart (Tong, 1988) demonstrates maximizing recovery efficiency during the low water-cut period (water cut <25%) is critical to optimizing the ultimate recovery (Figure 5). All fields with higher than 50% recovery factor have adopted effective reservoir management practices, such as horizontal wells, sand control, artificial lift, and well treatment (Table 7). Considering all these factors, there is a strong likelihood the published recoverable reserves of 400–800 million bbl of oil may have significant upside.

Table 4.

Knowledge Standardization of Key Geological and Engineering Parameters for the Zama and Amal Fields

Parameter CategoryStandardized ValueStandardized Value
1. Field
 Field nameZamaAmal
 CountryMexicoLibya
 Discovery year20171959
 First production year20221966
 Current statusAppraisalSecondary recovery
 Onshore or offshoreOffshoreOnshore
 Water depth, ft546Not applicable
2. Reservoir general
 Hydrocarbon typeOilOil
 Original reservoir pressure, psiNot available4675
 Pressure gradient, psi/ftNot available0.47
 Drive mechanisms (main)Not availableAquifer drive
3. Well
 Total producersNot available175
 Total injectorsNot available4
 Well typeNot availableVertical or deviated well
 Well spacing, acNot available210
 Initial well rate oil, BOPDNot available16,778
 Well EUR, thousand bbl of oilNot available6183
4. Trap
 Tectonic settingSaltRift
 Trapping mechanismDiapiric piercementPaleostructural subcrop
 Seismic anomalyAVO anomalyNone
 Structural compartment countNot available3
 Depth to top of reservoir, ft TVDML11,0009655
 Trap flank dip average, °Not available2
 Original productive area, ac3200156,900
 Hydrocarbon column height, ft3018720
5. Reservoir
 Reservoir unitZama SandstoneAmal–Maragh
 Reservoir ageLate MioceneCambrian–Triassic, Cretaceous
 Depositional environmentSubmarine fanBraided river
 Gross reservoir thickness (average), ft1676570
 Net reservoir thickness (average), ft1173Not available
 Net-to-gross ratio (average)0.7Not available
 Net pay (average), ft607Not available
 Reservoir lithologySandstoneSandstone
 Matrix porosity (average), %2514
 Air permeability (average), md4501
6. Fluid
 API gravity (average), ° API2936
 Initial GOR (average), SCF/STB450444
7. Resource
 Original oil in place, million bbl of oil1400–20005000
 EUR, million bbl of oil400–8001082
 Estimated ultimate recovery factor, %Not available22
Parameter CategoryStandardized ValueStandardized Value
1. Field
 Field nameZamaAmal
 CountryMexicoLibya
 Discovery year20171959
 First production year20221966
 Current statusAppraisalSecondary recovery
 Onshore or offshoreOffshoreOnshore
 Water depth, ft546Not applicable
2. Reservoir general
 Hydrocarbon typeOilOil
 Original reservoir pressure, psiNot available4675
 Pressure gradient, psi/ftNot available0.47
 Drive mechanisms (main)Not availableAquifer drive
3. Well
 Total producersNot available175
 Total injectorsNot available4
 Well typeNot availableVertical or deviated well
 Well spacing, acNot available210
 Initial well rate oil, BOPDNot available16,778
 Well EUR, thousand bbl of oilNot available6183
4. Trap
 Tectonic settingSaltRift
 Trapping mechanismDiapiric piercementPaleostructural subcrop
 Seismic anomalyAVO anomalyNone
 Structural compartment countNot available3
 Depth to top of reservoir, ft TVDML11,0009655
 Trap flank dip average, °Not available2
 Original productive area, ac3200156,900
 Hydrocarbon column height, ft3018720
5. Reservoir
 Reservoir unitZama SandstoneAmal–Maragh
 Reservoir ageLate MioceneCambrian–Triassic, Cretaceous
 Depositional environmentSubmarine fanBraided river
 Gross reservoir thickness (average), ft1676570
 Net reservoir thickness (average), ft1173Not available
 Net-to-gross ratio (average)0.7Not available
 Net pay (average), ft607Not available
 Reservoir lithologySandstoneSandstone
 Matrix porosity (average), %2514
 Air permeability (average), md4501
6. Fluid
 API gravity (average), ° API2936
 Initial GOR (average), SCF/STB450444
7. Resource
 Original oil in place, million bbl of oil1400–20005000
 EUR, million bbl of oil400–8001082
 Estimated ultimate recovery factor, %Not available22

Abbreviations: AVO = amplitude versus offset; EUR = estimated ultimate recovery; GOR = gas–oil ratio; SCF = standard cubic feet; STB = stock tank barrel; TVDML = true vertical depth below mudline.

Table 5.

List of Reservoir Analogues with Critical Parameters for the Understanding of Zama Development Concept

Field NameReservoir Unit NameCountryHydrocarbon TypeAir Permeability (Average), mdViscosity, cpNet Pay (Average), ftDrive Mechanism MainUltimate Recovery Factor, %
AgbamiAkata (13–18 m.y.)NigeriaOil only2700.26Not availableSolution gas48
AlbacoraCarapebusBrazilOil only100026.5Not availableSolution gas35
AtlantisMiddle Miocene SandsUnited StatesOil only1000Not available312Moderate aquifer25
BalderHeimdal–Hermod–BalderNorwayOil with gas60003.582Strong aquifer48
BarracudaMarlim-10BrazilOil only10002.8567Solution gas44
Belayim MarineRudeis–KareemEgyptOil only4001.12Not availableStrong aquifer46
BuzzardBuzzard SandstoneUnited KingdomOil only1900Not availableNot availableSolution gas58
CarpinteriaPico (Repettian Stage)United StatesOil only5508350Moderate aquifer, solution gasNot available
ClaymoreClaymore SandstoneUnited KingdomOil only1504.75518Weak aquifer, solution gas42
FoinavenVailaUnited KingdomOil with gas8004Not availableUnknown aquifer drive strength35
FortiesFortiesUnited KingdomOil only7000.76Not availableStrong aquifer62
GirassolMalembo (B Sand System)AngolaOil only42001.1300Weak aquifer47
JubileeMahogany SandGhanaOil only3000.23149Gravity drainage40
MagnusMagnus SandstoneUnited KingdomOil only3500.3213Solution gas54
MarsD-T1 SandsUnited StatesOil only311Not available440Compaction26
MillerBraeUnited KingdomOil only1000.2Not availableWeak aquifer, solution gas56
NamoradoNamoradoBrazilOil only3001.57Not availableWeak aquifer, solution gas59
NelsonFortiesUnited KingdomOil only200Not availableNot availableStrong aquifer61
RoncadorCarapebusBrazilOil with gas80010520Solution gas, gas cap expansion28
SchiehallionVailaUnited KingdomOil with gas12503Not availableSolution gas35
Thunder HorsePink–Brown–Peach (Ths)United StatesOil only770Not available390Strong aquifer27
West SenoUpper MioceneIndonesiaOil with gas3602.1226Solution gas25
Field NameReservoir Unit NameCountryHydrocarbon TypeAir Permeability (Average), mdViscosity, cpNet Pay (Average), ftDrive Mechanism MainUltimate Recovery Factor, %
AgbamiAkata (13–18 m.y.)NigeriaOil only2700.26Not availableSolution gas48
AlbacoraCarapebusBrazilOil only100026.5Not availableSolution gas35
AtlantisMiddle Miocene SandsUnited StatesOil only1000Not available312Moderate aquifer25
BalderHeimdal–Hermod–BalderNorwayOil with gas60003.582Strong aquifer48
BarracudaMarlim-10BrazilOil only10002.8567Solution gas44
Belayim MarineRudeis–KareemEgyptOil only4001.12Not availableStrong aquifer46
BuzzardBuzzard SandstoneUnited KingdomOil only1900Not availableNot availableSolution gas58
CarpinteriaPico (Repettian Stage)United StatesOil only5508350Moderate aquifer, solution gasNot available
ClaymoreClaymore SandstoneUnited KingdomOil only1504.75518Weak aquifer, solution gas42
FoinavenVailaUnited KingdomOil with gas8004Not availableUnknown aquifer drive strength35
FortiesFortiesUnited KingdomOil only7000.76Not availableStrong aquifer62
GirassolMalembo (B Sand System)AngolaOil only42001.1300Weak aquifer47
JubileeMahogany SandGhanaOil only3000.23149Gravity drainage40
MagnusMagnus SandstoneUnited KingdomOil only3500.3213Solution gas54
MarsD-T1 SandsUnited StatesOil only311Not available440Compaction26
MillerBraeUnited KingdomOil only1000.2Not availableWeak aquifer, solution gas56
NamoradoNamoradoBrazilOil only3001.57Not availableWeak aquifer, solution gas59
NelsonFortiesUnited KingdomOil only200Not availableNot availableStrong aquifer61
RoncadorCarapebusBrazilOil with gas80010520Solution gas, gas cap expansion28
SchiehallionVailaUnited KingdomOil with gas12503Not availableSolution gas35
Thunder HorsePink–Brown–Peach (Ths)United StatesOil only770Not available390Strong aquifer27
West SenoUpper MioceneIndonesiaOil with gas3602.1226Solution gas25
Table 6.

Analogue Characterization for the Zama Development Concept: Probabilistic Distribution of 90% to 10% Range of Analogues for the Key Numeric Parameters

Numeric ParameterMeanP90–P10 Range of Analogues
P90P50P10
Well spacing, ac18222170381
Initial well rate, BOPD10,7552412754225,200
Well EUR, million bbl of oil23111447
Plateau annual recovery, % of oil in place3.21.62.95.5
Plateau annual recovery, % of EUR7.84.66.812
Ultimate recovery factor, %42264360
Numeric ParameterMeanP90–P10 Range of Analogues
P90P50P10
Well spacing, ac18222170381
Initial well rate, BOPD10,7552412754225,200
Well EUR, million bbl of oil23111447
Plateau annual recovery, % of oil in place3.21.62.95.5
Plateau annual recovery, % of EUR7.84.66.812
Ultimate recovery factor, %42264360

Abbreviations: EUR = estimated ultimate recovery; P10 = estimate exceeded with 10% probability; P50 = estimate exceeded with 50% probability; P90 = estimate exceeded with 90% probability.

Table 7.

Analogue Characterization for the Zama Development Concept: Reservoir Management Best Practices for Fields with >50% Ultimate Recovery Factor

Reservoir Management Best PracticesFirstSecondThird
Secondary recovery methodContinuous water injection
Conformance improvementWater pluggingModifying injection patternProfile modification
DrillingHorizontal wellInfill drilling
Sand control methodStand-alone sand screenHydraulic fracturing and gravel packingCase-hole gravel pack
Artificial liftGas liftESP
Well treatmentScale inhibitor treatmentSand cleaningWax removal
Reservoir Management Best PracticesFirstSecondThird
Secondary recovery methodContinuous water injection
Conformance improvementWater pluggingModifying injection patternProfile modification
DrillingHorizontal wellInfill drilling
Sand control methodStand-alone sand screenHydraulic fracturing and gravel packingCase-hole gravel pack
Artificial liftGas liftESP
Well treatmentScale inhibitor treatmentSand cleaningWax removal

Abbreviation: — = none; ESP = electric submersible pump.

Figure 5.

Recovery efficiency against the empirical recovery chart (Tong, 1988) for the Zama development analogues with >50% ultimate recovery factor. Maximizing recovery efficiency during the low water-cut period is critical to optimizing the ultimate recovery.

Figure 5.

Recovery efficiency against the empirical recovery chart (Tong, 1988) for the Zama development analogues with >50% ultimate recovery factor. Maximizing recovery efficiency during the low water-cut period is critical to optimizing the ultimate recovery.

PRODUCTION APPLICATIONS

Globally, billions of barrels of oil can be monetized through the application of improved and enhanced recovery techniques as demonstrated by the successful polymer flood of several offshore giant fields (e.g., Captain field, United Kingdom, and Suizhong 36-1 field, China). Information derived from the use of appropriate analogues comprising the most successfully developed fields can help rejuvenate production and maximize ultimate recovery. Identifying opportunities for reserve growth requires a detailed knowledge of what the most efficient producers are doing under comparable geologic and engineering circumstances. Analogues can help determine which improved and enhanced oil recovery techniques are likely to be most efficient for a given reservoir. Benchmarking of geologic-engineering attributes, production performance, and recovery factor against global analogues can help discover critical issues and reveal new opportunities for improvement. Further analysis of the critical issues can help identify the best-performing analogues, lessons learned, and potential solutions to the specific production challenges.

Analogue intelligence has proven to be a powerful method to screen investment opportunities in mature (or even abandoned) fields. Specific technologies, such as horizontal and multilateral drilling, underbalanced drilling, or gravity-assisted thermal recovery, can be applied to reservoirs with appropriate geological and engineering parameters from analogue fields. This methodology improves decision quality and drives value and is herein demonstrated through a case study of Amal field, onshore Libya.

Redevelopment Opportunities for Amal Field

The Amal field, onshore Libya, has estimated STOIIP of 5 billion bbl and a very large productive area of 156,900 ac and produces from a tight sandstone reservoir with an average permeability of 1 md. After more than 45 yr of production, it has only recovered 18% of STOIIP (Figure 6A), while the majority of resources remain in the ground (Table 4). The analogue workflow recommended herein was employed to review the Amal field and benchmark its recovery factor against applicable global analogues to identify the best practices of fields with better and more efficient recovery. As with the other examples presented here, the workflow followed was as follows:

  • 1. Defining problems and objectives: Given the production challenge of low reservoir permeability, what is the upside potential for Amal redevelopment? What ideas could be implemented from fields with similar production challenges?

  • 2. Capturing knowledge: Based on public domain data sources, both the text and numeric parameters have been standardized and classified using consistent rules and a comprehensive classification scheme (Table 4).

  • 3. Analogue selection: Since the main objective in this case is to identify best practices and optimal solutions to the production challenges of a mature asset, analogue selection is therefore focused on hydrocarbon type (oil), development situation (onshore), reservoir lithology (sandstone), air permeability (<20 md), API gravity (>22°), and original oil in place (>500 million bbl of oil) as the critical search parameters. Twenty-one applicable global analogues were selected using search criteria relevant to the Amal redevelopment challenges (Table 8).

  • 4. Analysis and insights: Analysis of recovery efficiency using the empirical recovery chart, which describes the relationship between ultimate recovery, recovery to date, and water cut (Tong, 1988), indicates that an ultimate recovery factor of 40% could be possible for the Amal field (Figure 6B). Benchmarking of Amal field’s geologic-engineering parameters against applicable global analogues reveals several critical issues, including selection of a very large well spacing (i.e., small number of producers for its very large productive area) and poor recovery efficiency (Table 9). In addition, the field has adopted few modern reservoir management practices. The poor reservoir quality and weak natural energy drive for the analogous reservoirs mean application of improved recovery techniques and adoption of good reservoir management practices are critical to optimize the ultimate recovery of the low-permeability sandstone reservoirs. Data from analogue fields with more than 30% ultimate recovery suggest several successful secondary methods, including continuous water injection, hydrocarbon gas injection, and water-alternating-gas (WAG) immiscible injection, and conformance improvement techniques, such as modifying injection pattern, profile modification, and zonal injection (Table 10). The WAG miscible flood and CO2 miscible flood have also been successfully applied to several fields that have achieved higher recovery, such as Alpine, North Ward Estes, and Rangely fields, United States. Reservoir management best practices from those fields with more than 30% ultimate recovery include horizontal wells, infill drilling, hydraulic fracturing, matrix acidization, artificial lift, production optimization, and well treatment (Table 10). This analogue-based analysis allows the operator to evaluate the cost of improved recovery programs against the value of the potential remaining recoverable reserves and resources to more accurately determine the economic viability of this field redevelopment opportunity.

Figure 6.

Production performance of the Amal field, onshore Libya: (A) production history curve (1966–2010) and (B) recovery efficiency against empirical recovery chart (Tong, 1988). Amal field lies along 40% of the ultimate recovery trend, indicating the field has potential to recover 40% of stock tank oil initially in place given its rock and fluid properties, drive mechanism, and reservoir conditions.

Figure 6.

Production performance of the Amal field, onshore Libya: (A) production history curve (1966–2010) and (B) recovery efficiency against empirical recovery chart (Tong, 1988). Amal field lies along 40% of the ultimate recovery trend, indicating the field has potential to recover 40% of stock tank oil initially in place given its rock and fluid properties, drive mechanism, and reservoir conditions.

Table 8.

List of Reservoir Analogues with Critical Parameters for the Identification of Amal Redevelopment Opportunity

Field NameReservoir Unit NameCountryHydrocarbon TypeAir Permeability (Average), mdViscosity, cpNet Pay (Average), ftDrive Mechanism MainUltimate Recovery Factor, %
AhwazAsmariIranOil with gas100.58430Strong aquifer48
AlpineAlpine SandstoneUnited StatesOil only150.4550Solution gas50
Altamont–BluebellGreen River andColton/WasatchUnited StatesOil only0.1Not available575Solution gas12
AnsaiYanchangChinaOil only2246Solution gas25
Barrow IslandWindalia sandAustraliaOil with gas2.51.76Not availableSolution gas, gas cap expansion27
Bitkiv–BabcheMeniliteUkraineOil with gas51.3766Moderate aquifer14
BorislavMenilite–Popel–Wytwycia–Jamna–StryjUkraineOil only5.54169Unknown aquifer drive strength39
ChaoyanggouFuyu (Quantou)ChinaOil only11.310.429Solution gas21
ChicontepecChicontepecMexicoOil only15Not availableSolution gas8.5
DolinWyhoda–Bystrycia–Maniava–MeniliteUkraineOil only131289Moderate aquifer34
Hassi MessaoudZone RaAlgeriaOil only50.21210Solution gas24
LuginetsTyumen-NaunakRussiaOil with gas200.3Not availableSolution gas, gas cap expansion26
NakhlaUpper SarirLibyaOil only1.40.35260Solution gasNot available
North Ward EstesYatesUnited StatesOil with gas191.4100Solution gas40
OrocualSan JuanVenezuelaOil with gas5Not available520Solution gasNot available
PriobAchimov–VartovRussiaOil only3.61.93131Weak aquifer28
RangelyWeber sandstoneUnited StatesOil with gas81.7189Solution gas, unknown aquifer drive strength51
Rhourde El BaguelCambrianAlgeriaOil only51.55902Solution gas32
RostovtsevNovy PortRussiaOil with gas-condensate6Not availableNot availableSolution gas, gas cap expansion33
Spraberry TrendSpraberry–DeanUnited StatesOil only0.30.770Solution gas15
VakhNaunakRussiaOil only161.1552Solution gas33
Field NameReservoir Unit NameCountryHydrocarbon TypeAir Permeability (Average), mdViscosity, cpNet Pay (Average), ftDrive Mechanism MainUltimate Recovery Factor, %
AhwazAsmariIranOil with gas100.58430Strong aquifer48
AlpineAlpine SandstoneUnited StatesOil only150.4550Solution gas50
Altamont–BluebellGreen River andColton/WasatchUnited StatesOil only0.1Not available575Solution gas12
AnsaiYanchangChinaOil only2246Solution gas25
Barrow IslandWindalia sandAustraliaOil with gas2.51.76Not availableSolution gas, gas cap expansion27
Bitkiv–BabcheMeniliteUkraineOil with gas51.3766Moderate aquifer14
BorislavMenilite–Popel–Wytwycia–Jamna–StryjUkraineOil only5.54169Unknown aquifer drive strength39
ChaoyanggouFuyu (Quantou)ChinaOil only11.310.429Solution gas21
ChicontepecChicontepecMexicoOil only15Not availableSolution gas8.5
DolinWyhoda–Bystrycia–Maniava–MeniliteUkraineOil only131289Moderate aquifer34
Hassi MessaoudZone RaAlgeriaOil only50.21210Solution gas24
LuginetsTyumen-NaunakRussiaOil with gas200.3Not availableSolution gas, gas cap expansion26
NakhlaUpper SarirLibyaOil only1.40.35260Solution gasNot available
North Ward EstesYatesUnited StatesOil with gas191.4100Solution gas40
OrocualSan JuanVenezuelaOil with gas5Not available520Solution gasNot available
PriobAchimov–VartovRussiaOil only3.61.93131Weak aquifer28
RangelyWeber sandstoneUnited StatesOil with gas81.7189Solution gas, unknown aquifer drive strength51
Rhourde El BaguelCambrianAlgeriaOil only51.55902Solution gas32
RostovtsevNovy PortRussiaOil with gas-condensate6Not availableNot availableSolution gas, gas cap expansion33
Spraberry TrendSpraberry–DeanUnited StatesOil only0.30.770Solution gas15
VakhNaunakRussiaOil only161.1552Solution gas33
Table 9.

Analogues to Understand the Production Challenge for the Amal Field: Key Numeric Parameter Values of Target Reservoir Against Probabilistic Distribution of 90% to 10% Range of Analogues

Numeric ParameterAmal Field ValueP90–P10 Range of Analogues
P90P50P10
Productive area, ac156,90011,00056,7351,197,118
Total producers175184713344
Well spacing, ac2109.546280
Air permeability, md10.8517
Ultimate recovery factor, %22132849
Numeric ParameterAmal Field ValueP90–P10 Range of Analogues
P90P50P10
Productive area, ac156,90011,00056,7351,197,118
Total producers175184713344
Well spacing, ac2109.546280
Air permeability, md10.8517
Ultimate recovery factor, %22132849

Abbreviations: P10 = estimate exceeded with 10% probability; P50 = estimate exceeded with 50% probability; P90 = estimate exceeded with 90% probability.

Table 10.

Analogues to Understand the Production Challenge for the Amal Field: Reservoir Management Best Practices for Fields with >30% Ultimate Recovery Factor

Reservoir Management Best PracticesFirstSecondThird
Secondary recovery methodContinuous water injectionHydrocarbon gas injectionWAG immiscible injection
Enhanced oil recovery methodWAG miscible floodCO2 miscible floodHydrocarbon miscible flood
Conformance improvementModifying injection patternProfile modificationZonal injection
DrillingHorizontal wellInfill drilling
StimulationHydraulic fracturingMatrix acidization
Artificial liftRod pumpGas liftESP
Production optimizationRecompletionReperforationAdditional perforation
Well treatmentScale inhibitor treatmentCorrosion inhibitor treatment
Reservoir Management Best PracticesFirstSecondThird
Secondary recovery methodContinuous water injectionHydrocarbon gas injectionWAG immiscible injection
Enhanced oil recovery methodWAG miscible floodCO2 miscible floodHydrocarbon miscible flood
Conformance improvementModifying injection patternProfile modificationZonal injection
DrillingHorizontal wellInfill drilling
StimulationHydraulic fracturingMatrix acidization
Artificial liftRod pumpGas liftESP
Production optimizationRecompletionReperforationAdditional perforation
Well treatmentScale inhibitor treatmentCorrosion inhibitor treatment

Abbreviations: — = none; ESP = electric submersible pump; WAG = water-alternating-gas.

CONCLUSION

Global analogues have wide application in supplementing the technical understanding of geoscientists, reservoir engineers, and decision-makers working across the E&P life cycle. Analogues have specific application to new ventures, prospect generation, risk assessment, reserves booking, field development, production operations, and portfolio management. Properly selected analogues serve to constrain interpretations, inform choices and decisions, and benchmark asset performance. The power of analogues for both geoscientists and engineers stems from expanding knowledge from being colloquial, to a broader stance, derived from sources beyond individual or team experience. Rather than relying upon a single analogue or geographically close analogues, we recommend a comparison to a group of genetically related analogues to understand the full range of uncertainty. The value of global analogues can only be realized through the development of a coherent and consistent knowledge base and within a structured and classified knowledge framework. The ability to apply global analogues to a local situation can help add an additional dimension of creativity and confidence to E&P decision-making for the following applications:

  • Generate new exploration ideas: Identify most likely play and trap types in basins of interest and demonstrate what success looks like through understanding analogous discoveries; broaden the knowledge and experience base of individuals and teams, opening explorers’ minds as to what is possible.

  • Reduce exploration uncertainty: Calibrate prospect uncertainty ranges using key facts from commercially successful fields and deliver more confidence in prospect evaluation.

  • Validate development concepts: Subsurface analogues provide an objectivity basis on which to test the viability of field development scenarios and characterize permissible alternatives of a geologic model.

  • Evaluate opportunities for redevelopment: Understand the primary controls on recovery efficiency, test these against global best practices for recovery improvement, and use this knowledge to guide redevelopment and optimization of existing assets.

  • Benchmark production performance and recovery factor: Identify applicable global analogues to facilitate comparison of field performance against what is possible, understand what the best-performing analogues have done to maximize production efficiency, and establish recovery factor trends for a specific portfolio theme.

  • Rank the portfolio of your assets: Build a reservoir knowledge base unique to your portfolio; classify an existing portfolio of prospects, assets, or both; and identify portfolio issues and spotlight the best opportunities for value creation.

ACKNOWLEDGMENTS

We thank C&C Reservoirs for allowing access to its proprietary global field and reservoir knowledge base for this publication and the AAPG Editor and reviewers for their constructive comments and suggestions. We also thank Rod Sloan and Dave Jenkins for their comments on the earlier draft of the paper. We are grateful to Rena Yan, Yangyang Li, and KaDavien Baylor for their assistance during preparation of the paper.

Shaoqing Sun holds a B.Sc. degree in petroleum geology from Daqing Petroleum Institute, northeastern China, and a Ph.D. in reservoir geology from University of Reading, United Kingdom. Sun’s career portfolio includes being senior geoscientist for Petroleum Information (1990–1992) and consulting geologist for Chevron Corporation (1993–1994). He founded C&C Reservoirs in January 1995 and currently holds the position of chief geoscientist. He is the corresponding author of this paper.

David A. Pollitt is director of geoscience for C&C Reservoirs, a role he assumed in August 2019. Prior to this, he spent 12 years working for Chevron Corporation. Pollitt holds a B.Sc. degree and a Ph.D. in carbonate sedimentology and numerical modeling, both from Cardiff University, as well as an M.B.A. from Durham University, and he is a United Kingdom Chartered Geologist.

Shengyu Wu received his master’s degree and Ph.D. in geology and geophysics from Rice University. He has an undergraduate degree in electrical engineering. He has been working for C&C Reservoirs focusing on applications of analogues in exploration and production workflow since 2003. He worked for the Bureau of Geophysical Prospecting, China National Petroleum Corporation, supervising seismic data processing, Anschutz Exploration as a geoscientist, and Total USA as vice president of exploration.

David A. Leary recently retired as senior advisor from ExxonMobil, having led and advised teams in play analysis and business development around the world. He holds an M.A. degree and M.B.A. from The University of Texas, and a B.S. degree (geoscience) from Purdue University, where he was recently named Distinguished Science Alumnus. He is currently a visiting professor at Chengdu University of Technology.

REFERENCES CITED

1.
Amy
L. A.
,
2019
,
A review of producing fields inferred to have upslope stratigraphically trapped turbidite reservoirs: Trapping styles (pure and combined), pinch-out formation, and depositional setting
:
AAPG Bulletin
 , v. 
103
, no. 
12
, p. 
2861
2889
, doi:10.1306/02251917408.
2.
Bhushan
V.
Hopkinson
S. C.
,
2002
,
A novel approach to identify reservoir analogs
:
Society of Petroleum Engineers 13th European Petroleum Conference
 , Aberdeen, Scotland, October 29–31, 2002, SPE-78338-MS, 6 p.
3.
Brazil
E. V.
Segura
V.
Cerqueira
R.
Paula
R. D.
Mello
U.
,
2018
,
Visual analytics for reservoir analogues
:
AAPG Search and Discovery article 70361
 , accessed September 26, 2019, http://www.searchanddiscovery.com/documents/2018/70361brazil/ndx_brazil.pdf.
4.
Dailly
P.
Henderson
T.
Hudgens
E.
Kanschat
K.
Lowry
P.
,
2012
,
Exploration for Cretaceous stratigraphic traps in the Gulf of Guinea, West Africa and the discovery of the Jubilee Field: A play opening discovery in the Tano Basin, offshore Ghana
:
Geological Society, London, Special Publications 2012
 , v. 
369
, p. 
235
248
, doi:10.1144/SP369.12.
5.
Dolson
J.
He
Z. Y.
Horn
B. W.
,
2018
,
Advances and perspectives on stratigraphic trap exploration—Making the subtle trap obvious
:
AAPG Search and Discovery article 60054
 , accessed September 26, 2019, http://www.searchanddiscovery.com/documents/2018/60054dolson/ndx_dolson.pdf.
6.
Hodgin
J. E.
Harrell
D. R.
,
2006
,
The selection, application, and misapplication of reservoir analogs for the estimation of petroleum reserves
:
Society of Petroleum Engineers Annual Technical Conference and Exhibition
 , San Antonio, Texas, September 24–27, 2006, SPE-102505-MS, 15 p.
7.
Howell
J. A.
Martinius
A. W.
Good
T. R.
,
2014
,
The application of outcrop analogues in geological modelling: A review, present status and future outlook
:
Geological Society, London, Special Publications 2014
 , v. 
387
, p. 
1
25
, doi:10.1144/SP387.12.
8.
Jewell
G.
,
2011
,
Exploration of the transform margin of west Africa
:
Discovery thinking—Jubilee and beyond & Exploration of the Tano Basin and discovery of the Jubilee Field
 , Ghana: A new deepwater game-changing hydrocarbon play in the transform margin of west Africa: AAPG Search and Discovery article 110156, accessed September 26, 2019, http://www.searchanddiscovery.com/documents/2011/110156jewel/ndx_jewel.pdf.
9.
Offshore Energy Today
,
2019
,
Talos cheers another success at Zama discovery offshore Mexico
, accessed September 26, 2019, https://www.offshoreenergytoday.com/talos-cheers-another-success-at-zama-discovery-offshore-mexico/.
10.
Offshore Technology
,
2017
, Zama oilfield development, accessed September 26, 2019, https://www.offshore-technology.com/projects/zama-oil-discovery/.
11.
Perrons
R. K.
Jensen
J. W.
,
2015
,
Data as an asset: What the oil and gas sector can learn from other industries about “big data”
:
Energy Policy
 , v. 
81
, p. 
117
121
, doi:10.1016/j.enpol.2015.02.020.
12.
Rodriguez
H. M.
Escobar
E.
Embid
S.
Rodriguez
N.
Hegazy
M.
Lake
L. W.
,
2013
,
A new approach to identify analogue reservoirs
:
Society of Petroleum Engineers Annual Technical Conference and Exhibition
 , New Orleans, Louisiana, September 30–October 2, 2013, SPE-166449-MS, 17 p.
13.
Sidle
R. E.
Lee
W. J.
,
2010
,
An update on the use of reservoir analogs for the estimation of oil and gas reserves
:
Society of Petroleum Engineers Hydrocarbon Economics and Evaluation Symposium
 , Dallas, Texas, March 8–9, 2010, SPE-129688-MS, 9 p.
14.
Society of Petroleum Engineers
,
2018
,
Petroleum resources management system
:
Richardson
 , Texas, Society of Petroleum Engineers, 57 p.
15.
Sun
S. Q.
Wan
J. C.
,
2002
,
Geological analogs usage rates high in global survey
:
Oil & Gas Journal
 , v. 
100
, no. 
46
, p. 
49–50
.
16.
Temizel
C.
Dursun
S.
,
2013
,
Efficient use of methods, attributes, and case-based reasoning algorithms in reservoir analogue techniques in field development
:
Society of Petroleum Engineers Digital Energy Conference and Exhibition
 , Woodlands, Texas, March 5–7, 2013, SPE-163700-MS, 19 p.
17.
Tong
X. Z.
,
1988
,
Statistical rules of natural and artificial water drive reservoirs
, in
Tong
X. Z.
, ed.,
Analysis of oil well production performance and reservoir behavior
 , 1st ed.:
Beijing
, China, Publishing House of Documentation for Science and Technology, p. 
56
92
.
18.
Volpi
B.
Piantanida
M.
Bernorio
D.
D’andrea
M. G.
Morando
P.
Nardon
S.
,
2003
,
Characterization by analogues
:
An innovative approach to reservoir study: Offshore Mediterranean Conference and Exhibition
 , Ravenna, Italy, March 26–28, 2003, 9 p.
Gold Open Access. This paper is published under the terms of the CC-BY license.