Abstract The stochastic stratigraphic well correlation method considers the stratigraphic correlation of well data as a set of possible models to sample and manage uncertainty in subsurface studies. This method was applied to the Malampaya buildup (a well documented offshore gas field located NW of the Palawan Island, Philippines), aged upper Eocene to lower Miocene. Previous studies highlight that rock petrophysical properties are mainly controlled by diagenesis. Correlation rules are thus developed in order to adapt the stochastic stratigraphic well correlation method to the study of diagenetic units. These rules are based on wireline log shape and diagenetic units types. Four stratigraphic correlation models are generated using the proposed correlation method: a deterministic one corresponding to the most probable model considering only well data and three stochastic ones. These correlation models are bound with geostatistical methods to build static reservoir models. Synthetic seismic profiles are computed from facies models conditioned to acoustic impedance models. It leads to comparable seismic amplitude images, highlighting the importance of considering several well correlation models for one given seismic survey. Stochastic stratigraphic correlations are shown to have a first-order impact on reservoir unit characterization, rock volumes and fluid flow response on the reservoir model.

Abstract Fault-related dolomite subsurface reservoirs are formed from fluid circulation that results in significant transformation of the reservoir properties. The geometry and internal organization of such dolomitic reservoirs remain difficult to image with seismics alone. A multi-scale approach is essential to understand and predict the diagenetic processes that control the exact 3D morphology of the dolomite with spatial precision and true dimensions, and consequently the reservoir properties. In this context, we propose an analytical workflow including field work, LIDAR scanning and numerical geology applied to dolomite outcrops in Mesozoic carbonates (SE France). The exposed dolomite-limestone contact exhibits sinuous, irregular and convolute shapes, which are either fault-parallel, bedding-parallel or chaotic. To characterize this complex distribution, we performed LIDAR scanning on 500 m x 150 m cliffs and road cuts with 4.5 cm to 1–1.5 cm average point spacing. The cloud is composed of 22 millions points comprising X, Y, Z, intensity, red, green, and blue attributes. Digitization of the limestone-dolomite boundary was performed in RiscanPro and GOCAD environments, for extracting the true 3D geometry of the dolomite body for further geostatistical and 3D facies modelling. This approach captures the large-scale geometry of the dolomite bodies. However, single RGB or intensity properties do not unequivocally reproduce small-scale (below ∼ 1 m) heterogeneities of the late diagenetic dolomite. Color changes induced by weathering or climatic conditions are of the same size range as the small-scale heterogeneities, thus they are not unique to allow automated tracking on the point set. As a result, the workflow remains time-consuming, and further work is needed to allow calibration of the LIDAR data points with mineralogy.

Abstract Carbonate platforms can have complex internal facies variations and stratal geometries expressed at length scales longer than all but the largest outcrops. The latter commonly form high and relatively inaccessible cliffs, and thus conventional field techniques (logging and photomontages) may not adequately capture the 3D geometry of surfaces and the details of the facies distribution. Because facies and stratal geometry control rock properties and connectivity in carbonate reservoirs, accurate outcrop data can be critical to reservoir and forward seismic modeling. The Gresse-en-Vercors cliffs (southeastern France) provide a seismic-scale slice though a Lower Cretaceous (Barremian) platform margin analogous to Lower Cretaceous reservoirs in the Middle East. The cliffs are 500 m high and extend for 25 km along depositional dip, straddling the transition from shallow-water platform to deeper basin. This paper describes the methodology developed to create a high-resolution stratigraphical digital outcrop model (DOM) integrating field measurements (logged sections, facies mapping) and high-resolution digital data (photomosaic and new LIDAR data acquired by a helicopter survey). Integration of the LIDAR and other point cloud data provide a high-resolution digital elevation model (DEM) on which georeferenced field observations were then posted. The “solid image” technique was used to extract precise x,y,z coordinates of stratigraphic surfaces from the DEM. The resulting numerical geological model allows a coherent restoration of the platform architecture, quantification of component surfaces (shape, angles, dimensions) and geobodies, and a better characterization of the relationship between facies and platform architecture