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Petroleum Data Analytics (PDA)—Application of AI Machine Learning in Petroleum Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (10 August 2021) | Viewed by 8375

Special Issue Editor

Prof. Dr. Shahab D. Mohaghegh

E-Mail Website
Guest Editor
Department of Petroleum and Natural Gas Engineering, West Virginia University, Morgantown, WV 26506, USA
Interests: artificial neural networks; evolutionary computing and fuzzy logic in earth science; reservoir engineering; natural gas engineering; simulation and modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Petroleum Data Analytics (PDA) is the application of Artificial Intelligence and Machine Learning in petroleum engineering-related problem solving and decision-making. PDA will fully control the future of science and technology in the petroleum industry. It is highly important for the new generation of engineers, scientists, and petroleum professionals to develop a realistic scientific understanding of this technology.

Similar to the application of Artificial Intelligence and Machine Learning in other engineering-related disciplines, Petroleum Data Analytics addresses two major issues that determine the success or failure of this technology in petroleum industry: (a) differences in how AI and ML should be applied to engineering versus non-engineering-related problems and decision-making, and (b) how AI and ML is differentiated from traditional statistical analysis. Lack of success or mediocre outcomes of AI and ML in petroleum industry has been quite common. To a large degree, this has to do with a superficial understanding of this technology by some petroleum engineering domain experts and concentration on marketing schemes rather than science and technology.

Prof. Shahab D. Mohaghegh
Guest Editor

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Keywords

  • artificial intelligence
  • machine learning
  • data science
  • data analytics
  • petroleum engineering
  • oil and gas industry
  • drilling engineering
  • reservoir engineering
  • geo-science
  • completion engineering
  • production engineering

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Published Papers (2 papers)

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Research

32 pages, 9825 KiB  
Article
The Efficacy and Superiority of the Expert Systems in Reservoir Engineering Decision Making Processes
by Turgay Ertekin
Appl. Sci. 2021, 11(14), 6347; https://doi.org/10.3390/app11146347 - 9 Jul 2021
Cited by 3 | Viewed by 2838
Abstract
In the process of making a critical decision in reservoir engineering, most of the time we find ourselves in a quandary. Like in any other scientific or technical field, when we find ourselves having to make a critical decision at a juncture, we [...] Read more.
In the process of making a critical decision in reservoir engineering, most of the time we find ourselves in a quandary. Like in any other scientific or technical field, when we find ourselves having to make a critical decision at a juncture, we cannot go ahead with our gut feelings, but rather must figure out what knowledge and information is lacking. In generating the missing knowledge and understanding, the depth and the rapid nature of the search will surface as two critical parameters. In other words, most of the time, a shallow search that can be conducted in a short period of time will not produce the missing information and the knowledge and more often, possibly, it will provide misguidance. When a large volume of sources of information is reviewed and the missing knowledge is generated using unbiased deductive methodologies, then, one can make an informed decision based on facts rather than intuition. In achieving such a desired result, it will be necessary to use fast algorithmic protocols to not sacrifice the wide nature of the search domain, to ensure that it is possible to generate the desired solution. In this paper, it is shown how in reservoir engineering desirable decisions can be reached in a timely manner choosing the most appealing course of action. It is true that in reservoir engineering applications, the decision-making process may involve a blend of intuition and scientific and rational thinking, critical factors such as blind spots, and the use of conventional methodologies that make decision-making hard to fully operationalize or to get a handle on. Luckily, there are mathematical and computational tools to ensure that scientists/engineers consistently make correct decisions, which include gathering as much information as possible and considering all possible alternatives (like combinatorial analysis protocols). The tool (model) proposed in this paper for making critical reservoir engineering decisions is a new computational platform/protocol that exploits the advantages of mathematically developed formulations and of the models that are based on the data/information collected. It is furthermore shown that the analyses conducted, and critical decisions reached, represent more thorough and far-reaching solutions that are structured using less computational overhead, thereby increasing the quality of the decision even further. Full article
(This article belongs to the Special Issue Petroleum Data Analytics (PDA)—Application of AI Machine Learning in Petroleum Engineering)
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26 pages, 13498 KiB  
Article
One-Dimensional Convolutional Neural Network with Adaptive Moment Estimation for Modelling of the Sand Retention Test
by Nurul Nadhirah Abd Razak, Said Jadid Abdulkadir, Mohd Azuwan Maoinser, Siti Nur Amira Shaffee and Mohammed Gamal Ragab
Appl. Sci. 2021, 11(9), 3802; https://doi.org/10.3390/app11093802 - 22 Apr 2021
Cited by 7 | Viewed by 2594
Abstract
Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using [...] Read more.
Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R2, while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy. Full article
(This article belongs to the Special Issue Petroleum Data Analytics (PDA)—Application of AI Machine Learning in Petroleum Engineering)
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