An Improved Method of Reservoir Facies Modeling Based on Generative Adversarial Networks

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of “unconditional-conditional simulation cooperation” (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application.

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network

Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficult to characterize the complex sedimentary microfacies structure. Multi-point geostatistics (MPG) method can learn a priori geological model and can realize multi-point correlation simulation in space, while deep neural network can express nonlinear relationship well. This article comprehensively utilizes the advantages of the two to try to optimize the multi-point geostatistical reservoir facies modeling algorithm based on the Deep Forward Neural Network (DFNN). Through the optimization design of the multi-grid training data organization form and repeated simulation of grid nodes, the simulation results of diverse modeling algorithm parameters, data conditions and deposition types of sedimentary microfacies models were compared. The results show that by optimizing the organization of multi-grid training data and repeated simulation of nodes, it is easier to obtain a random simulation close to the real target, and the simulation of sedimentary microfacies of different scales and different sedimentary types can be performed.

Object methods of geostatistical analysis for facies modeling

Facies cube is a required part of a static model, especially concerning fields characterized by complicated geological structure. The important quantitative limitations for modeling are facies proportions in the formation volume. Nowadays these proportions are calculated using standard geostatistical methods without considering particular properties of facies data. These properties are specific geometrical characteristics of sedimentological units. The consequences are significant differences between calculated and actual data and unreliable hydrocarbon reserves estimation.In order to enhance reliability of reserves estimation on the basis of 3D static models, this article is devoted to special methods of geostatistical analysis for facies data: object geometrization and object clustering. These methods allow taking into account specific geometrical parameters of formations deposited in different environments, therefore, allow reducing differences between calculated and actual facies data and enhancing reliability of reserves estimation.

3D Facies Modeling of Mishrif Reservoir in Halfaya Oil Field

This research deals with the study of the types and distribution of petrographic microfacies and Paleoenvironments of Mishrif Formation in Halfaya oil field, to define specific sedimentary environments. These environments were identified by microscopic examination of 35 thin sections of cutting samples for well HF-9H as well as 150 thin sections of core and cutting samples for well HF-I. Depending on log interpretation of wells HF-1, HF-316, HF-109, IIF-115, and IIF-272, the sedimentary facies were traced vertically through the use of various logs by Petrel 2013 software in addition to previous studies. Microfacies analysis showed the occurrence of six main Paleoenvironments within Mishrif succession, represented by basin, slope, shoal, Rudist biostrome, back shoal, and lagoon. Mishrif Formation was divided into six reservoir units depending on well logs and CPI. These units are separated by low porosity and high-water saturation of barrier beds. The reservoir beds from top to bottom are MA, MB1, MB2, MCI, and MC2. The reservoir units MB1, MB2, and MCI are the most important in the field of interest due to good reservoir properties and being the principle oil-bearing units in Mishrif Formation.

Reservoir facies design and modeling using probabilistic rock-physics templates

We have developed a technique to design and optimize reservoir lithofluid facies based on probabilistic rock-physics templates. Subjectivity is promoted to design possible facies scenarios with different pore-fluid conditions, and quantitative simulations and evaluations are conducted in facies model selection. This method aims to provide guidelines for reservoir-facies modeling in an exploration setting in which limited data exist. The work includes two parts: facies-model simulations and uncertainty evaluations. We have first derived scenarios with all possible fluid types using Gassmann fluid substitution. We designed models with different numbers of facies and pore-fluid conditions using site-specific rock-physics templates. Detailed facies simulations were conducted in the petroelastic, elastic, and seismic domains in a step-by-step framework to preserve the geologic interpretability. The use of probabilistic rock-physics templates allowed for multiple realizations of each facies model to account for different types and magnitudes of errors and to infer facies probability and uncertainty. For each realization, we used Bayesian classification to assign facies labels. Comparisons between the predicted and true labels provided the success rates and entropy indices to quantify the prediction errors and confidence degrees, respectively. This workflow was tested with well-log data from a clastic reservoir in the Gulf of Mexico. We simulated models with five to seven facies with different pore-fluid parameters. From the petroelastic, elastic, and seismic domains, the uncertainty of facies models significantly increased due to well-log measurement errors, data-model mismatch, and resolution differences. The facies model consisting of oil sand, gas sand, and shale was the optimal set based on the high success rates and low entropy indices. Facies profiles estimated from this optimal model presented significant consistency with well-log interpretations. The techniques and results demonstrated here could be applied to different types of clastic reservoirs, and they provide useful constraints for reservoir facies modeling during early oilfield exploration stages.