Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Nikravesh, Masoud; Kovscek, A. R.; Murer, A.S.; Patzek, Tadeusz W

doi:10.2118/35721-ms

Cited by 16 publications

(3 citation statements)

References 25 publications

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“…The paper [8] describes a new approach to managing large projects for pumping liquid into dense reservoirs with hydraulic fracturing. Neural networks were used to analyze the past results of flooding projects and predict future oil recovery, as well as water injection and production.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

Daribayev

Mukhanbet

Nurakhov

et al. 2021

EEJET

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The problem of oil displacement was solved using neural networks and machine learning classifiers. The Buckley-Leverett model is selected, which describes the process of oil displacement by water. It consists of the equation of continuity of oil, water phases and Darcy’s law. The challenge is to optimize the oil displacement problem. Optimization will be performed at three levels: vectorization of calculations; implementation of classical algorithms; implementation of the algorithm using neural networks. A feature of the method proposed in the work is the identification of the method with high accuracy and the smallest errors, comparing the results of machine learning classifiers and types of neural networks. The research paper is also one of the first papers in which a comparison was made with machine learning classifiers and neural and recurrent neural networks. The classification was carried out according to three classification algorithms, such as decision tree, support vector machine (SVM) and gradient boosting. As a result of the study, the Gradient Boosting classifier and the neural network showed high accuracy, respectively 99.99 % and 97.4 %. The recurrent neural network trained faster than the others. The SVM classifier has the lowest accuracy score. To achieve this goal, a dataset was created containing over 67,000 data for class 10. These data are important for the problems of oil displacement in porous media. The proposed methodology provides a simple and elegant way to instill oil knowledge into machine learning algorithms. This removes two of the most significant drawbacks of machine learning algorithms: the need for large datasets and the robustness of extrapolation. The presented principles can be generalized in countless ways in the future and should lead to a new class of algorithms for solving both forward and inverse oil problems

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

Daribayev

Mukhanbet

Nurakhov

et al. 2021

EEJET

View full text Add to dashboard Cite

show abstract

“…The ability techniques, such as neural network and fuzzy logic, to work with noisy data and solve problems even if information related to detailed physics of the system is not known or the system is too complex to be solved by traditional formal methods has provided new means of addressing these complex processes. Artificial intelligence, and both fuzzy logic and neural networks in particular, can give the petroleum industry new tools for better understanding and controlling recovery processes and therefore achieving efficient and profitable oil recovery [1,2] .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Waterflooding Through Application of Neural Networks

Nakutnyy¹,

Asghari²,

Törn³

2008

Canadian International Petroleum Conference

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Petroleum reservoirs demonstrate a very complex behavior that changes with time in a non-linear manner. Application of neural networks for field-wise analysis of waterflooding projects is very appropriate because a structural model between injection and production does not need to be specified in order to predict performance. The neural network approach recognizes that individual well behavior may depend on the well history and the injection/production conditions of surrounding wells. The outcome of this neural network analysis could determine injection and production policies that would lead to determining the minimum injection water leading to maximum oil recovery.

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“…Recently, artificial neural networks have been used in reservoir characterization to model permeability in old fields 9 , steam and water injection, 10 waterflooding in lowpermeability-fractured oil reservoirs, 11 and model oil and water imbibition processes. 12 Neural networks have a number of attractive features that can be beneficial to solving reservoir characterization problems.…”

Section: Introductionmentioning

confidence: 99%

Development Of Reservoir Characterization Models Using Core, Well Log, and 3D Seismic Data and Intelligent Software

B.¹,

Holditch²

1999

Proceedings of SPE Eastern Regional Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal component and factor analyses to reduce the dimensionality and help the neural networks formulate more realistic models.We have used an intelligent software, Oilfield Intelligence (OI) 1 , as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. We validated OI by obtaining confident models in three different oil field problems: (1) a neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas 1 , (2) a neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas 2 . This paper presents the models that we developed using OI in C-4 and C-5 for the VLE 196 Lamar Field, Block V in Lake Maracaibo. We found that the best neural network permeability model should have as input variables porosity and gamma ray log. The coefficient of determination of that model is 0.9756 with an average absolute error of 18.03%. From non-linear regression and non-parametric approach methods, the absolute error of these models varies from 38.1 to 97.78%. For the same reservoir, we developed a high-quality, multivariate, and neural network model to predict the pseudo-gamma ray log from 3D seismic attributes. The data points used during the training, testing, and validation comprised approximately 11,800 points. Two dime/depth conversion tables were used to convert the arrival time of seismic to depth. We developed a Fortran-90 program to make that conversion. The confidence of that model is around 90% with an expected absolute error of 11%.In all cases, we compared the neural network models with the non-linear regression and non-parametric approach models. The results show that one of the ways in the near future to get better reservoir characterization models is using neural networks in conjunction with the multivariate statistical techniques.

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Neural Networks for Field-Wise Waterflood Management in Low Permeability, Fractured Oil Reservoirs

Cited by 16 publications

References 25 publications

Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

Analysis of Waterflooding Through Application of Neural Networks

Development Of Reservoir Characterization Models Using Core, Well Log, and 3D Seismic Data and Intelligent Software

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