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The Longdong area in the Ordos basin is a typical fluvial reservoir with strong heterogeneity. In order to clarify the distribution law of underground reservoirs in the Longdong area, it is necessary to establish and optimize a 3D geological model to characterize the heterogeneity of reservoirs. This is of great significance for accelerating the exploitation of tight sandstone gas in the southwest of the Ordos basin. This study takes the P2h8 member of the Ct3 research area in the Longdong area as an example, analyzes the core and logging curve shape to divide the sedimentary microfacies, and establishes the facies model. In particular, in view of the difficulty in obtaining 3D training images under the existing conditions in the study area, we use the multi-point geostatistics method combining sequential two-dimensional condition simulation and the direct sampling method to establish the facies model. This method can simulate the 3D geological model by using the 2D training images composed of the digital plane facies diagrams and the well-connection facies diagrams. In addition, we choose the object-based method and sequential indicator method for comparative experiments to verify the feasibility of this method (sequential two-dimensional condition simulation combined with the direct sampling method) from many aspects. The results show that the multi-point geostatistics method based on 2D training images can not only match the well data, but also show the geometric characteristics and contact relationship of the simulation object. The distribution characteristics of sandbody thickness and modeling results are consistent with the actual geological conditions in the study area. This study explores the feasibility of this method in the 3D geological simulation of large-scale fluvial facies tight sandstone reservoirs. Additionally, it also provides a new idea and scheme for the modeling method of geologists in similar geological environments. [ABSTRACT FROM AUTHOR]

Geological facies modeling has long been studied to predict subsurface resources. In recent years, generative adversarial networks (GANs) have been used as a new method for geological facies modeling with surprisingly good results. However, in conventional GANs, all layers are trained concurrently, and the scales of the geological features are not considered. In this study, we propose to train GANs for facies modeling based on a new training process, namely progressive growing of GANs or a progressive training process. In the progressive training process, GANs are trained layer by layer, and geological features are learned from coarse scales to fine scales. We also train a GAN in the conventional training process, and compare the conventionally trained generator with the progressively trained generator based on visual inspection, multi-scale sliced Wasserstein distance (MS-SWD), multi-dimensional scaling (MDS) plot visualization, facies proportion, variogram, and channel sinuosity, width, and length metrics. The MS-SWD reveals realism and diversity of the generated facies models, and is combined with MDS to visualize the relationship between the distributions of the generated and training facies models. The conventionally and progressively trained generators both have very good performances on all metrics. The progressively trained generator behaves especially better than the conventionally trained generator on the MS-SWD, MDS plots, and the necessary training time. The training time for the progressively trained generator can be as small as 39% of that for the conventionally trained generator. This study demonstrates the superiority of the progressive training process over the conventional one in geological facies modeling, and provides a better option for future GAN-related researches. [ABSTRACT FROM AUTHOR]

Subsurface models are central pieces of information in different earth-related disciplines such as groundwater management and hydrocarbon reservoir characterization. These models are normally obtained using geostatistical simulation methods. Recently, methods based on deep learning algorithms have been applied as subsurface model generators. However, there are still challenges on how to include conditioning data and ensure model variability within a set of realizations. We illustrate the potential of Generative Adversarial Networks (GANs) to create unconditional and conditional facies models. Based on a synthetic facies dataset, we first train a Deep Convolution GAN (DCGAN) to produce unconditional facies models. Then, we show how image-to-image translation based on a U-Net GAN framework, including noise-layers, content loss function and diversity loss function, is used to model conditioning geological facies. Results show that GANs are powerful models to capture complex geological facies patterns and to generate facies realizations indistinguishable from the ones comprising the training dataset. The U-Net GAN framework performs well in providing variable models while honoring conditioning data in several scenarios. The results shown herein are expected to spark a new generation of methods for subsurface geological facies with fragmentary measurements. [ABSTRACT FROM AUTHOR]

This study showcases the value of drone photogrammetry in creating a meter-scale geological model of complex carbonate geobodies. Although drone photogrammetry is now commonly used for modeling the sedimentary facies and architecture of sandstone outcrops, its use is not widespread in creating geomodels of carbonate geobodies. Drone photogrammetry can generate accurate line-drawing correlation and detailed architecture analysis along inaccessible vertical faces of outcrops and permits observations of unreachable places. This work models the Bathonian limestones of Massangis quarry (Burgundy) as an example. The quarry covers an area of 0.4 km2 and is considered as an analogue for the Oolithe Blanche geothermal reservoir in the center of the Paris Basin. The Massangis quarry model represents a good analogue for reservoir microporosity and secondary porosity associated with dedolomitization. Ten facies are described and grouped into three facies associations (1) clinoforms, (2) tidal to subtidal facies, and (3) lagoonal facies. The clinoforms are sets of very large marine dunes 15–20 m high that prograded N70° across the platform as part of a regressive systems tract. Moldic rhombohedral pore spaces associated with dedolomitization are well-expressed within clinoforms and in the bioturbated levels of lagoonal facies. Drone photogrammetry combined with the "Truncated Gaussian with Trends" algorithm implemented in Petrel® software is used to create a geological model that faithfully reproduces the facies architecture observed in the quarry cliffs. Drone photogrammetry can be combined with field work to describe and locate facies and so constrain the spatial distribution of petrophysical properties. It also helps constraining the shapes of geobodies in the model grid for more realistic geological static models and helps providing 3D petrophysical models from an outcropping analogue for geothermal and petroleum reservoirs. Image 1 • Drone-based photogrammetry in combination with VRGS® and Petrel® softwares. • High-resolution Digital Outcrop Modeling of 3D facies and sedimentary structures. • Clinoforms from giant, 15–20 m high marine sand waves prograding on the platform. • Analogue for carbonate-based geothermal and petroleum reservoirs. [ABSTRACT FROM AUTHOR]

The current work aims to study the structural and facies characterizations of the Apollonia Formation carbonate reservoir by applying the curvature attribute and facies modeling. This reservoir is considered an unconventional, gas-bearing chalk reservoir located at the JDT field in the Abu El-Gharadig Basin, northern Western Desert of Egypt. The workflow begins by computing the curvature seismic attribute on the available 3D seismic data of the field. This is accompanied by the detailed sedimentological and petrophysical analysis of available core data and the distribution of the resulting facies log along the available 3D geological model. The results of the curvature attribute along the Apollonia "A5" layer showed faults traces more than three times the traces determined by the variance attribute along the same layer, with small horizontal spacing between faults about 230 m. This attribute is considered as the best attribute for determining subseismic resolution faults (SSRF) of the polygonal faults affecting the reservoir. The facies analysis indicated that this chalk reservoir is composed of four primary facie: Faceis A (clean chalk), Facies B (slightly argillaceous chalk), Facies C (argillacesou chalk) and Facies D (marl). These facies had been differentiated form each other based on the clay percentage, porosity and permeability. The best reservoir facies are Facies A (clean chalk) with a clay percentage 0–5%, average porosity 31% and average permeability 0.43mD and Facies B (slightly argillaceous chalk) with a clay percentages 5–15%, average porosity 23% and average permeability 0.12mD. Distributing these facies along the JDT field declares dramatically that facies characterization of the lower pay zone is better than the upper pay zone because of the abundance of A- and B-type facies along the lower zone. Consequently, a 3D geological model had been constructed which used for the determination of the best location of any further development well targeting primarily A- and B-type facies and for any future production and hydraulic fracturing operations. • Application of Curvature attributes for the structural characterization of highly faulted carbonate reservoir. • Construction of 3D facies modeling for the lithological characterization of the carbonate reservoir. • The impact of these method on the reservoir development. [ABSTRACT FROM AUTHOR]