Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks

Deeva, Irina; Bubnova, Anna; Andriushchenko, Petr; Voskresenskiy, Anton; Bukhanov, N.; Nikitin, Nikolay O.; Kalyuzhnaya, Anna V.

doi:10.1007/978-3-030-77961-0_33

Cited by 2 publications

(4 citation statements)

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“…The main issue is only to determine a suitable measure of proximity or distance to the target reservoir. The main difficulties in choosing a metric are described in our previous work [18], and here we present the best metrics for use in our experiments.…”

Section: Search For Similar Reservoirs 41 Search Methods Reservoirs A...mentioning

confidence: 99%

“…Such weights can be obtained by two approaches: 1) with the help of specialized knowledge of domain experts or 2) with the help of statistical analysis and optimization methods. In our previous work [18], we tried to solve this problem by introducing an additional regularization for continuous parameters using estimation weights. For this purpose, we relied on the analysis of, for example, the Gower distance, for which the penalties for discrete parameters are on average higher than for continuous parameters.…”

Section: Search For Similar Reservoirs 41 Search Methods Reservoirs A...mentioning

confidence: 99%

“…With uniform sampling, all data is divided into equal intervals. The kmeans algorithm for discretization implements the k-means clustering algorithm [18]. The use of mathematical discretization methods is due to the need to use a single unified discretization method and the ability to discretize not only by the uniform distribution (because when we use geological information for discretization, this forms uniform distributions).…”

Section: Discretization Typementioning

confidence: 99%

“…BNs are already used in the oil and gas industry, for example, to identify the significance of geological parameters [32,31]. BNs can also be used to reveal new knowledge about an object, fill in gaps, and find anomalous values [18,3]. This paper discusses an approach based on the BN construction for the problem of modelling and predicting reservoir parameters (fig.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Andriushchenko¹,

Deeva²,

Bubnova³

et al. 2022

Preprint

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The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks (BNs). Recovery forecasting is critical for the oil and gas industry as it directly affects a company's profit. However, current approaches to forecasting the RF are complex and computationally expensive. In addition, they require vast amount of data and are difficult to constrain in the early stages of reservoir development. To address this problem, we propose a BN approach and describe ways to improve parameter predictions' accuracy. Various training hyperparameters for BNs were considered, and the best ones were used. The approaches of structure and parameter learning, data discretization and normalization, subsampling on analogues of the target reservoir, clustering of networks and data filtering were considered. Finally, a physical model of a synthetic oil reservoir was used to validate BNs' predictions of the RF. All approaches to modelling based on BNs provide full coverage of the confidence interval for the RF predicted by the physical model, but at the same time require less time and data for modelling, which demonstrates the possibility of using in the early stages of reservoirs development. The main result of the work can be considered the development of a methodology for studying the parameters of reservoirs based on Bayesian networks built on small amounts of data and with minimal involvement of expert knowledge. The methodology was tested on the example of the problem of the recovery factor imputation.

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Section: Search For Similar Reservoirs 41 Search Methods Reservoirs A...mentioning

confidence: 99%

Section: Search For Similar Reservoirs 41 Search Methods Reservoirs A...mentioning

confidence: 99%

Section: Discretization Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Andriushchenko¹,

Deeva²,

Bubnova³

et al. 2022

Preprint

Self Cite

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Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models

2023

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Bayesian networks are a powerful tool for modelling multivariate random variables. However, when applied in practice, for example, for industrial projects, problems arise because the existing learning and inference algorithms are not adapted to real data. This article discusses two learning and inference problems on mixed data in Bayesian networks—learning and inference at nodes of a Bayesian network that have non-Gaussian distributions and learning and inference for networks that require edges from continuous nodes to discrete ones. First, an approach based on the use of mixtures of Gaussian distributions is proposed to solve a problem when the joint normality assumption is not confirmed. Second, classification models are proposed to solve a problem with edges from continuous nodes to discrete nodes. Experiments have been run on both synthetic datasets and real-world data and have shown gains in modelling quality.

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Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks

Cited by 2 publications

References 16 publications

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Advanced Approach for Distributions Parameters Learning in Bayesian Networks with Gaussian Mixture Models and Discriminative Models

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