Analysis of parameters of oil and gas fields using Bayesian networks

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

doi:10.3997/2214-4609.202054026

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pre-learning stage participation of expert include (1) detailed choice of learning algorithms, parameters to model and discretization methods, (2) direct inclusion and exclusion of particular edges of networks which are explicitly defined by expert and are taken into account during learning procedure, (3) filtering option which allows to create networks for particular basins, stratigraphy and ranges of parameters. Post-learning stage include: (1) usage of created networks as a monitoring tool for particular reservoir to be sure that all assumed relations are in place and validated by data obtained on a field, (2) rapid forecast of possible recovery factor and other parameters for a particular region with limited information, (3) comparison between different networks which allow to delineate robust relations across different geological settings. However, there are also downsides to this approach: since this model relies almost entirely on data, the BN requires good quality data for reservoirs parameters.…”

Section: Description Of Approaches To Recovery Factor Estimationmentioning

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 1 more Smart Citation

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

Andriushchenko¹,

Deeva²,

Bubnova³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Description Of Approaches To Recovery Factor Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Andriushchenko¹,

Deeva²,

Bubnova³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In this section, we summarize, in abbreviated form, the pros and cons of methods that are used for modelling and could potentially be the basis for reservoir analysis. They are described in more detail in our previous work [1].…”

Section: Related Workmentioning

confidence: 99%

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

Deeva¹,

Bubnova²,

Andriushchenko³

et al. 2021

Preprint

View full text Add to dashboard Cite

In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows analysing parameters of a reservoir, discovery dependencies among parameters (including cause and effects relations), checking for anomalies, prediction of expected values of missing parameters, looking for the closest analogues, and much more. The method is based on extended algorithm MixLearn@BN for structural learning of Bayesian networks. Key ideas of MixLearn@BN are following: (1) learning the network structure on homogeneous data subsets, (2) assigning a part of the structure by an expert, and (3) learning the distribution parameters on mixed data (discrete and continuous). Homogeneous data subsets are identified as various groups of reservoirs with similar features (analogues), where similarity measure may be based on several types of distances. The aim of the described technique of Bayesian network learning is to improve the quality of predictions and causal inference on such networks. Experimental studies prove that the suggested method gives a significant advantage in missing values prediction and anomalies detection accuracy. Moreover, the method was applied to the database of more than a thousand petroleum reservoirs across the globe and allowed to discover novel insights in geological parameters relationships.

show abstract

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

Deeva

Bubnova

Andriushchenko

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Analysis of parameters of oil and gas fields using Bayesian networks

Cited by 3 publications

References 0 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

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

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

Contact Info

Product

Resources

About