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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: closed (25 May 2023) | Viewed by 20419

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Special Issue Editors

Prof. Dr. Vasily Demyanov

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Guest Editor

School of Energy, Geoscience, Infrastructure and Society, Institute for GeoEnergy Engineering, Heriot-Watt University, Edinburgh EH14 4AS, UK
Interests: geodata science; uncertainty quantification in prediction modelling; inverse modelling for history matching; stochastic optimisation; advanced geostatistical techniques; machine learning for spatial modelling

Dr. Leonardo Azevedo

E-Mail Website
Guest Editor

Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, 1049-001 Lisben, Portugal
Interests: geostatistics; seismic reservoir characterisation; geophysical inversion and data integration; programming activities

Special Issue Information

Dear Colleagues

Recent trends in reservoir modelling continue to show a keen interest in machine learning and data mining applications. This is fueled by the rapid advances in computer science and a constantly increasing volume and variety of reservoir data that have become available in the digital era.

A Special Issue of Energies, an open access MDPI journal (IF: 2.702; CiteScore: 3.8), aims to support the dissemination and exchange of recent progress on this topic.

We would like to invite original research contributions to the Special Issue that will cover machine learning and data mining applications including but not limited to the following topics:

Analysis, inference and integration of core samples and imaging data;
Knowledge discovery and integration from outcrop and analogue data;
Seismic inversion with machine learning;
Automatic seismic interpretation and integration into geo-modelling workflows;
Machine learning for discrete and continuous reservoir property modelling;
Reservoir flow prediction modelling and uncertainty—learning from data;
Decision making based on information and knowledge mined from large reservoir data sets.

Prof. Dr. Vasily Demyanov
Dr. Leonardo Azevedo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Machine learning
Spatial data science
Data mining
Geodata interpretation
Pattern and knowledge discovery
Reservoir pattern recognition
Seismic inversion
Flow prediction
Uncertainty
Optimisation
Decision making

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Published Papers (7 papers)

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Research

23 pages, 7124 KiB

Open AccessArticle

Influence of Rock Properties on Structural Failure Probability—Caprock Shale Examples from the Horda Platform, Offshore Norway

by Md Jamilur Rahman, Manzar Fawad and Nazmul Haque Mondol

Energies 2022, 15(24), 9598; https://doi.org/10.3390/en15249598 - 17 Dec 2022

Cited by 4 | Viewed by 2088

Abstract

In any geological subsurface fluid injection, a viable top seal is required to contain the vertical movement of the injected fluid plume. However, seal integrity assessment is challenging because of the uncertainties possessed by various parameters. A probabilistic solution might be more appropriate when significant uncertainties are present. In this study, we evaluate Drake shale caprock structural reliability using a stochastic method. Drake shale is the primary top seal in the Aurora CO₂ storage site, located in the Horda Platform area in the northern North Sea. Based on the lithological variations, Drake caprock shale is divided into two parts designated by upper and lower units. Six model scenarios from the upper and lower Drake units have been tested. The probabilistic structural failures of varying model scenarios are estimated using the First-Order Reliability Method (FORM). Drake Formation shale shows a considerably low probability of failure (~0) with a high reliability index in the initial stress-state condition and after-injection scenarios. Moreover, the parameter sensitivity study indicates that horizontal stress and cohesion are the most influential input parameters during reliability estimation. Comparative analysis between the caprock properties and failure probability reveals that rock strength properties such as cohesion and friction angle strongly dictate the probability of failure estimation. Moreover, comparing two caprock shale formations indicates that the structural failure values are not correlatable; hence, a formation-specific failure assessment is recommended. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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13 pages, 4199 KiB

Open AccessArticle

Seismic Description of Deep Strike-Slip Fault Damage Zone by Steerable Pyramid Method in the Sichuan Basin, China

by Qingsong Tang, Shuhang Tang, Bing Luo, Xin Luo, Liang Feng, Siyao Li and Guanghui Wu

Energies 2022, 15(21), 8131; https://doi.org/10.3390/en15218131 - 31 Oct 2022

Cited by 8 | Viewed by 1699

Abstract

Large quantities of gas resources have been found in the Paleo-Mesozoic carbonate rocks in the Sichuan Basin. However, many wells cannot obtain high production in deep low porosity-permeability reservoirs. For this contribution, we provide a steerable pyramid method for identifying the fault damage zone in the Kaijiang–Liangping platform margin, which is infeasible by conventional seismic methods. The results show that steerable pyramid processing could enhance the seismic fault imaging and a series of NW-trending strike-slip faults are found along the trend of the carbonate platform margin. The steerable pyramid attribute presents distinct vertical and horizontal boundaries of the fault damage zone, and heterogeneous intensity of an un-through-going damage zone. The width of the fault damage zone is generally varied in the range of 100–500 m, and could be increased to more than 1000 m in the fault overlap zone, intersection area, and fault tips. Further, the fault damage zone plays a constructive role in the high gas production in the deep tight carbonate reservoir. The results suggest the steerable pyramid method is favorable for identifying the weak strike-slip faults and their damage zone. The width of the fault damage zone is closely related to fault displacement, and the much wider damage zone is generally influenced by the fault overlapping and interaction. The fractured reservoirs in the fault damage zone could be a new favorable exploitation domain in the Sichuan Basin. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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23 pages, 12288 KiB

Open AccessArticle

Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning

by Rachel E. Brackenridge, Vasily Demyanov, Oleg Vashutin and Ruslan Nigmatullin

Energies 2022, 15(3), 1070; https://doi.org/10.3390/en15031070 - 31 Jan 2022

Cited by 1 | Viewed by 2969

Abstract

Large databases of legacy hydrocarbon reservoir and well data provide an opportunity to use modern data mining techniques to improve our understanding of the subsurface in the presence of uncertainty and improve predictability of reservoir properties. A data mining approach provides a way to screen dependencies in reservoir and fluid data and enable subsurface specialists to estimate absent properties in partial or incomplete datasets. This allows for uncertainty to be managed and reduced. An improvement in reservoir characterisation using machine learning results from the capacity of machine learning methods to detect and model hidden dependencies in large multivariate datasets with noisy and missing data. This study presents a workflow applied to a large basin-scale reservoir characterization database. The study aims to understand the dependencies between reservoir attributes in order to allow for predictions to be made to improve the data coverage. The machine learning workflow comprises the following steps: (i) exploratory data analysis; (ii) detection of outliers and data partitioning into groups showing similar trends using clustering; (iii) identification of dependencies within reservoir data in multivariate feature space with self-organising maps; and (iv) feature selection using supervised learning to identify relevant properties to use for predictions where data are absent. This workflow provides an opportunity to reduce the cost and increase accuracy of hydrocarbon exploration and production in mature basins. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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23 pages, 11080 KiB

Open AccessArticle

Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

by Sergei Petrov, Tapan Mukerji, Xin Zhang and Xinfei Yan

Energies 2022, 15(3), 1064; https://doi.org/10.3390/en15031064 - 31 Jan 2022

Cited by 6 | Viewed by 2875

Abstract

The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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23 pages, 15482 KiB

Open AccessArticle

Generalized Extreme Value Statistics, Physical Scaling and Forecasts of Oil Production from All Vertical Wells in the Permian Basin

by Wardana Saputra, Wissem Kirati and Tadeusz Patzek

Energies 2022, 15(3), 904; https://doi.org/10.3390/en15030904 - 26 Jan 2022

Cited by 4 | Viewed by 2970

Abstract

We analyze nearly half a million vertical wells completed since the 1930s in the most prolific petroleum province in the U.S., the Permian Basin. We apply a physics-guided, data-driven forecasting approach to estimate the remaining hydrocarbons in these historical wells and the probabilities of well survival. First, we cluster the production data set into 192 spatiotemporal well cohorts based on 4 reservoir ages, 6 sub-plays, and 8 completion date intervals. Second, for each cohort, we apply the Generalized Extreme Value (GEV) statistics to each year of oil production from every well in this cohort, obtaining historical well prototypes. Third, we derive a novel physical scaling that extends these well prototypes for several more decades. Fourth, we calculate the probabilities of well survival and observe that a vertical well in the Permian can operate for 10–100 years, depending on the sub-play and reservoir to which this well belongs. Fifth, we estimate the total field production of all existing vertical wells in the Permian by replacing historical production from each well with its prototype. We then time-shift and sum up these prototypes together, obtaining 34 billion barrels of oil as estimated ultimate recovery (EUR). Our most notable finding is that the rate of finding big reservoirs in the Permian has been declining drastically and irreversibly since the 1970s. Today, operators need to drill wells that are twice as deep as the 1930s’ wells, yet they produce 4–12 times less. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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22 pages, 6685 KiB

Open AccessArticle

Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects

by Bastian Steffens, Quentin Corlay, Nathan Suurmeyer, Jessica Noglows, Dan Arnold and Vasily Demyanov

Energies 2022, 15(3), 902; https://doi.org/10.3390/en15030902 - 26 Jan 2022

Cited by 1 | Viewed by 3206

Abstract

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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23 pages, 6105 KiB

Open AccessArticle

Efficient Dimensionality Reduction Methods in Reservoir History Matching

by Amine Tadjer, Reider B. Bratvold and Remus G. Hanea

Energies 2021, 14(11), 3137; https://doi.org/10.3390/en14113137 - 27 May 2021

Cited by 3 | Viewed by 2568

Abstract

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach. Full article

(This article belongs to the Special Issue Data Science in Reservoir Modelling Workflows)

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