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Energies
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Reservoir Modeling and Simulation with Machine Learning and Data Mining
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Reservoir Modeling and Simulation with Machine Learning and Data Mining

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".

Deadline for manuscript submissions: closed (10 July 2021) | Viewed by 14652

Special Issue Editor

Dr. Manouchehr Haghighi

E-Mail Website
Guest Editor
Australian School of Petroleum & Energy Resources, University of Adelaide, Adelaide, Australia
Interests: reservoir simulation; data analytics; machine learning; reservoir characterisation

Special Issue Information

Dear Colleagues,

Reservoir simulation is the backbone of many decision-making processes in the oil and gas industry. Topics such as history matching, uncertainty quantification and production optimisation are key research areas in petroleum engineering and geosciences. Although advances in research on physics-based models are growing, the development of approximate proxy models based on data analytics is in high demand in research activities on multi-phase flow simulation in subsurface formations. With recent development in computer hardware and super computation, new techniques such as deep learning algorithms have received attraction among researchers in resource engineering, computer science and geoscience, and other related fields.

This Special Issue aims to collect original research or review articles on different aspects of reservoir simulation, data analytics and machine learning. Different types of reservoir simulation and proxy modelling for hydrocarbon reservoirs, water resources, CO2 sequestration and unconventional resources will be considered.

Dr. Manouchehr Haghighi
Guest Editor

Manuscript Submission Information

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Keywords

  • machine learning
  • data mining and data analytics
  • proxy modelling
  • dynamic reservoir flow simulation
  • reservoir characterisation
  • geo-model development
  • case studies on artificial neural networks
  • case studies in reservoir simulation
  • geo-model development

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Published Papers (4 papers)

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Research

16 pages, 7956 KiB  
Article
An Improved Method of Reservoir Facies Modeling Based on Generative Adversarial Networks
by Qingbin Liu, Wenling Liu, Jianpeng Yao, Yuyang Liu and Mao Pan
Energies 2021, 14(13), 3873; https://doi.org/10.3390/en14133873 - 28 Jun 2021
Cited by 13 | Viewed by 3044
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Reservoir Modeling and Simulation with Machine Learning and Data Mining)
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47 pages, 11111 KiB  
Article
Two-Step Predict and Correct Non-Intrusive Parametric Model Order Reduction for Changing Well Locations Using a Machine Learning Framework
by Hardikkumar Zalavadia and Eduardo Gildin
Energies 2021, 14(6), 1765; https://doi.org/10.3390/en14061765 - 22 Mar 2021
Cited by 6 | Viewed by 2565
Abstract
The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optimization to gain significant [...] Read more.
The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a two-step PMOR procedure, where, in the first step, a Proper Orthogonal Decomposition (POD)-based strategy that is non-intrusive to the simulator source code is introduced, as opposed to the convention of using POD as a simulator intrusive procedure. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML)-based framework used with POD. The features of the ML model (Random Forest was used here) are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoid simulator access for the time dependency of the solutions. The proposed PMOR method is global, since a single reduced-order model can be used for all the well locations of interest in the reservoir. We address the major challenge of the explicit representation of the well location change as a parameter by introducing geometry-based features and flow diagnostics-inspired physics-based features. In the second step, an error correction model based on reduced model solutions is formulated to correct for discrepancies in the state solutions at well grid blocks expected from POD basis for new well locations. The error correction model proposed uses Artificial Neural Networks (ANNs) that consider the physics-based reduced model solutions as features, and is proved to reduce the error in QoI (Quantities of Interest), such as oil production rates and water cut, significantly. This workflow is applied to a simple homogeneous reservoir and a heterogeneous channelized reservoir using a section of SPE10 model that showed promising results in terms of model accuracy. Speed-ups of about 50×–100× were observed for different cases considered when running the test scenarios. The proposed workflow for Reduced-Order Modeling is “non-intrusive” and hence can increase its applicability to any simulator used. Additionally, the method is formulated such that all the simulation time steps are independent and hence can make use of parallel resources very efficiently and also avoid stability issues that can result from error accumulation over time steps. Full article
(This article belongs to the Special Issue Reservoir Modeling and Simulation with Machine Learning and Data Mining)
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14 pages, 3756 KiB  
Article
3D Reservoir Geological Modeling Algorithm Based on a Deep Feedforward Neural Network: A Case Study of the Delta Reservoir of Upper Urho Formation in the X Area of Karamay, Xinjiang, China
by Jianpeng Yao, Qingbin Liu, Wenling Liu, Yuyang Liu, Xiaodong Chen and Mao Pan
Energies 2020, 13(24), 6699; https://doi.org/10.3390/en13246699 - 18 Dec 2020
Cited by 11 | Viewed by 2978
Abstract
Three-dimensional (3D) reservoir geological modeling is an advanced reservoir characterization method, which runs through the exploration and the development process of oil and gas fields. Reservoir geological modeling is playing an increasingly significant role in determining the distribution, internal configuration, and quality of [...] Read more.
Three-dimensional (3D) reservoir geological modeling is an advanced reservoir characterization method, which runs through the exploration and the development process of oil and gas fields. Reservoir geological modeling is playing an increasingly significant role in determining the distribution, internal configuration, and quality of a reservoir as well. Conventional variogram-based methods such as statistical interpolation and reservoir geological modeling have difficulty characterizing complex reservoir geometries and heterogeneous reservoir properties. Taking advantage of deep feedforward neural networks (DFNNs) in nonlinear fitting, this paper compares the reservoir geological modeling results of different methods on the basis of an existing lithofacies model and seismic data from the X area of Karamay, Xinjiang, China. Adopted reservoir geological modeling methods include conventional sequential Gaussian simulation and DFNN-based reservoir geological modeling method. The constrained data in the experiment mainly include logging data, seismic attribute data, and lithofacies model. Then, based on the facies-controlled well-seismic combined reservoir geological modeling method, this paper explores the application of multioutput DFNN and transfer learning in reservoir geological modeling. The results show that the DFNN-based reservoir geological modeling results are closer to the actual model. In DFNN-based reservoir geological modeling, the facies control effect is obvious, and the simulation results have a higher coincidence rate in a test well experiment. The feasibility of applying multioutput DFNN and transfer learning in reservoir geological modeling provides solutions for further optimization methods, such as solving small-sample problems and improving the modeling efficiency. Full article
(This article belongs to the Special Issue Reservoir Modeling and Simulation with Machine Learning and Data Mining)
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19 pages, 9540 KiB  
Article
Modeling Three-Dimensional Anisotropic Structures of Reservoir Lithofacies Using Two-Dimensional Digital Outcrops
by Yiming Yan, Liqiang Zhang and Xiaorong Luo
Energies 2020, 13(16), 4082; https://doi.org/10.3390/en13164082 - 6 Aug 2020
Cited by 6 | Viewed by 2935
Abstract
Reservoir heterogeneity is a key geological problem that restricts oil and gas exploration and development of clastic rocks from the early to late stages. Existing reservoir heterogeneity modeling methods such as multiple-point geostatistics (MPS) can accurately model the two-dimensional anisotropic structures of reservoir [...] Read more.
Reservoir heterogeneity is a key geological problem that restricts oil and gas exploration and development of clastic rocks from the early to late stages. Existing reservoir heterogeneity modeling methods such as multiple-point geostatistics (MPS) can accurately model the two-dimensional anisotropic structures of reservoir lithofacies. However, three-dimensional training images are required to construct three-dimensional reservoir lithofacies anisotropic structures models, and the method to use reservoir heterogeneity model of fewer-dimensional to obtain a three-dimensional model has become a much-focused research topic. In this study, the outcrops of the second member of Qingshuihe Formation (K1q2) in the northwestern margin of the Junggar Basin, which are lower cretaceous rocks, were the research target. The three-dimensional reservoir heterogeneity model of the K1q2 outcrop was established based on the unmanned aerial vehicle (UAV) digital outcrops model and MPS techniques, and the “sequential two-dimensional conditioning data” (s2Dcd) method was modified based on a sensitivity parameter analysis. Results of the parametric sensitivity analysis revealed that the isotropic multigrid simulations demonstrate poor performance because of the lack of three-dimensional training images, conditioning data that are horizontally discrete and vertically continuous, and distribution of lithofacies that are characterized by large horizontal continuities and small thicknesses. The reservoir lithofacies anisotropic structure reconstructions performed well with anisotropic multigrids. The simulation sequence of two-dimensional surfaces for generating the three-dimensional anisotropic structure of reservoir lithofacies models should be reasonably planned according to the actual geological data and limited hard data. In additional to this, the conditional probability density function of each two-dimensional training image should be fully utilized. The simulation results using only one two-dimensional section will have several types of noises, which is not consistent with the actual geological background. The anisotropic multigrid simulations and two-dimensional training image simulation sequence, proposed in this paper as “cross mesh, refinement step by step”, effectively reduced the noise generated, made full use of the information from the two-dimensional training image, and reconstructed the three-dimensional reservoir lithofacies anisotropic structures models, thus conforming to the actual geological conditions. Full article
(This article belongs to the Special Issue Reservoir Modeling and Simulation with Machine Learning and Data Mining)
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