### Evaluating and Combining Multiple-objective Ventures

In many prospects and proposed trends or contract areas, there may be multiple-zone, or multiple-play candidates. Some multiple ventures may be simple involving only two objectives and with clear independent and/or shared attributes. Other multiple ventures may be quite complex involving many objectives and with complex and subtle issues of partial dependencies further complicated by issues of marginal commerciality.

Sometimes a prospective contract area may contain an existing trend having some remaining exploration potential, as well as a second, deeper play, or another play in a different part of the exploration area. Some plays may be part of a single petroleum system involving partial dependencies among some or all geologic chance factors. In some prospective contract areas there may also be one or more producing fields to consider as well.

Several practical principles of multiple ventures should be emphasized:

As the number of objectives in any multiple venture increases arithmetically, the complexity of combination increases geometrically;

Monte Carlo or Latin Hypercube simulation is required for all but the simplest multiple-objective ventures;

Geologic dependency is common, especially involving the hydrocarbon charge and closure chance factors;

Many apparently complex ventures can be simplified and approximated by practical, common sense methods of consideration;

Analysts are well advised to focus on objectives that are “stand-alone commercial or economic” and ignore those that provide only incremental cash flows augmenting primary producing objectives; and

For comprehensive treatment of multiple-objective ventures, the reader is referred to James Murtha's excellent 1995 SPE paper.

Following the principle that a picture (or example) is worth a thousand words, two examples are provided herein, which should provide the careful reader with a good understanding of the concepts and procedures used in evaluating multiple-objective ventures. Part 1 describes several aspects of a two-objective multizone prospect, in which Monte Carlo simulation is avoided by basing calculation on only the mean reserves case of each objective. Part 2 describes a multiple venture involving two plays and a producing property, which employs a decision-tree analysis as well as Monte Carlo simulation.

#### Part 1: Multiple-zone Prospect Example: Geologic and Economic Assumptions

**Description** Prospect with two objectives, A & B. Dry-hole cost = $1 MM. Note: In this example, only four geologic chance factors are employed. Also, geologic chance of suc-cess (Pg) = commercial chance of success (Pc).

**Objective A** Low-relief anticlinal closure, carbonate reservoir resting on mature (?) source rock, capped by evaporiti topseal. Mean reserves estimated 2 MMBOE, equiv. PV = $10 MM.

**Objective B** Low-relief anticlinal closure, sandstone reservoir overlain by marine shale top seal, also mature (?) source rock. Mean reserves est. 10MM BOE, equiv. PV=$20MM.

Similar to prospect above, except that the HC source rock is positioned in the upper half of the marine shale unit, directly beneath the carbonate reservoir (A), so that it does not rest directly upon the sandstone reservoir (B). Accordingly there may be some reduced chance of hydrocarbon emplacement in the sandstone reservoir, because of the difficulty of “downcharging” against the natural buoyancy of hydrocarbons in formation water, as well as the difficulty of oil migrating through a considerable thickness of tight shale to access the sandstone reservoir rock.

##### Combining Reserves Distributions

In this example, the reserves distributions of the two prospects have not been combined, because the P10%, P50%, and P90% values were not provided, only the means). So, for the “both A & B” productive cases, reserves were combined simply by adding the two means, which is not strictly accurate.

Properly, reserves distributions of the two prospects, represented by P10%, P50%, and P90% values, can be combined by Monte Carlo simulation. However, this requires combining chance-weighted distributions through

*addition,*not*multiplication,*as we did graphically in Appendix B, with the three components of prospect reserves (Area, Average Net Pay, and HC-recovery Factor). Unfortunately, adding probabilistic distributions is not practical to perform graphically, so we should rely on Monte Carlo simulation to carry it out.

#### Part 2: Combining Multiple Types of Ventures*

This multiple-venture example illustrates the procedural steps involved in combining three objectives—a producing property, a medium-risk new play, and a high-risk new play (Section A).** It employs a probability tree for the partially dependent plays, as well as a Monte Carlo simulation. The probability tree solution (Sections B.l and B.2) uses only mean reserves outcomes for the different cases, yielding a total expected mean outcome for reserves of 36.10 MMBOE, having a total expected net present value (ENPV) of $261.7 MM. Section C, the Monte Carlo simulation (50,000 trials), shows the probability range of possible reserves outcomes. The distribution of reserves outcomes for all possible cases is shown as a frequency distribution (or probability-density graph). The P90% outcome is 11.19 MMBOE, the P50% outcome is 35.49 MMBOE, the P10% outcome is 153.22 MMBOE, and the mean outcome is 36.72 MMBOE, which agrees well with the 36.10 MMBOE figure derived using the probability tree and mean values (Section B). Thinking about the overall venture in a simplistic way, the P90% outcome expresses a scenario in which the producing property low-side case turns out to be the only project of the three that is successful, even marginally. The P50% case (35.49 MMBOE) represents an outcome in which the producing property turns out to be the P50% to P_{MEAN} case, and one of the two plays emerges successfully, providing “mid-range” reserves («P50% to P_{MEAN} if Play “Beta,” or P80% - P60% reserves if Play “Gamma”). The P10% outcome for the overall venture would either require all three ventures to succeed moderately, or the producing property and one of the two plays to succeed at relatively high probability levels.

Similarly, part D shows Monte Carlo simulation of probabilistic ENPV for the multiple venture outlined in part A, employing the appropriate NPV/BOE for the different subventures (“Alpha,” “Beta,“ and ”Gamma”). In other words, part D utilizes all the probabilistic outcomes of the entire venture in terms of ENPV, rather than reserves, and allows the ENPV of all possible reserves outcomes to be expressed.

##### Descriptions and Assumptions

###### Producing Field “Alpha” Prob. = 100%:

A producing field discovered and developed several years ago. Projected additional recoveries (some to be added through additional development and more efficient technology; anticipated capital investments ($15MM) included in $7 NPV/bbl figure):

###### New Play “Beta”:

A moderate-risk, moderate-potential, relatively shallow play having chance of play success = **0.72,** chance of average economic prospect success = *0.34,* four tests committed, so chance of at least one economic prospect success is *1 - (1 -.34) ^{4} = 0.81,* so chance of economic play success =

*0.72*×

*0.81 = 0.58:*Cost of play failure is $20MM.

###### Provisional New Play “Gamma”:

A high-risk, high-potential, deeper play, having chance of play success = *0.4 or 0.8,* chance of average economic prospect success *= 0.17,* three tests committed, so chance of at least one prospect success is 1 - *(1 - 0.17) ^{3} = 0.42,* so chance of cumulative economic play success =

*0.8 or 0.4*×

*.42 = 0.336 or 0.168.*There is partial dependency between Play Gamma and Play Beta, involving the shared chance factor HC source rocks: if Play Beta proves that HC source rocks are present, then the chance of HC source rocks will increase for Gamma to 0.8; if HC source rocks are absent in Beta, chance of HC source rocks will decline to 0.4 at Gamma. Cost of play failure is $30MM.

##### Combining Ventures (Nonstochastic Methods)

Probability Tree (see

**Figure D-l**).Expected Net Present Values of different play outcome combinations, based on mean reserves case only:

##### Stochastic Method of Combining Expected Reserves of All Ventures

Probabilistic distribution of Total Reserves, employing Monte Carlo simulation of all ventures' reserves distributions, and estimated venture chances of success and failure (producing property Alpha Pg = 100%). (See Figure **D-2;** note use of “90% = small” convention.)

##### Stochastic Method of Combining ENPVs of All Ventures

Expected Net Present Value of different play-outcome combinations, employing Monte Carlo simulation of all ventures' reserves distributions, anticipated NPV/bbl for different ventures, and estimated venture chances of success and failure (producing property “Alpha” Pg = 100%). (See **Figure D-3**; note use of “P90% = small” convention.)

^{*}The assistance of Robert V. Clapp is acknowledged with gratitude; Clapp prepared the probability tree (Section B) and carried out the Monte Carlo simulations (Sections C and D). Note that the distributions were not truncated above PI% and that Clapp utilized the “PID = small” convention.

^{**}Chance of geologic success based upon five chance factors; also, chance of geologic success = chance of commercial success in these examples.

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*contents*

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### Figures & Tables

### Contents

# Risk Analysis and Management of Petroleum Exploration Ventures

During the 1990s, many international petroleum companies improved their exploration performance significantly by using principles of risk analysis and portfolio management, in combination with new geotechnologies. While exploration risk cannot be eliminated, it can certainly be reduced substantially, on a portfolio scale. And the widespread adoption of standardized risk analysis methods during the 1990s brought badly needed discipline to petroleum exploration. By the mid-1980s, most well-informed major international petroleum firms that were engaged in exploration recognized that, globally, the average size of new discoveries was diminishing. Not coincidentally, the class of exploratory prospects categorized as “high risk/high-potential” was showing marked signs of underperformance. For major companies, when all such ventures, which averaged around a 10% perceived probability of success, were considered, less than 1% actually discovered profitable oil and gas reserves, and the sizes of these discoveries were generally far smaller than predicted. All in all, such exploration for new giant fields destroyed value, rather than creating it, in the 1980s and early 1990s. Consequently, exploration, as a corporate function, lost credibility. It badly needed to begin delivering on its corporate promises. It needed to become more efficient, and thereby more profitable. To optimize the allocation of exploration capital, concepts of portfolio management began to be considered.