Neural network applications to reservoirs: Physics-based models and data models

International audienceEditoria

Trends and Patterns in Artificial Intelligence Research for Oil and Gas Industry: A Bibliometric Review

Purpose: This paper aims to outline a broad-spectrum perspective of the structure of research in artificial intelligence (AI), in the oil and gas industry (OGI) based on bibliometric and distance-based visualisation of similarities (VOS) analysis.   Theoretical framework: The OGI has been one of the major contributors to the world economy. With the increasing energy demand, it has become necessary for the industry to adopt the latest technologies to enhance efficiency, reduce costs, and improve safety. One such technology is AI, which has the potential to revolutionise OGI.   Design/methodology/approach: The paper uses the data from Scopus online database as of April 2023. Based on “key-terms” search results, 251 valid documents were obtained for further analysis using VOS viewer software and Harzing’s Publish or Perish for citation metrics and analysis.   Findings: The finding shows that the Journal of Petroleum Science and Engineering is the field's most relevant journal, with 14 (5.58) published Articles. The People's Republic of China is the most productive country regarding AI research in the OGI. El-Sebakhy's (2009) article is the most cited article, with 113 citations and an average of 8.07 citations per year.   Research, Practical & Social implications: AI could transform OGI. Thus, adopting AI technologies can increase efficiency, reduce costs, and improve safety, also may increase productivity and economic benefits in AI research-intensive countries.   Originality/value: This study provides a comprehensive analysis of the existing AI research in the OGI, utilising bibliometric data and graphical networks

(FUZZY LOGIC AND APLLICATIONS IN GEOPHYSICS: A SEISMOLOGY EXAMPLE

Bulanık mantık, teknolojinin de etkisiyle son yıllarda birçok problemin çözümünde yaygın olarak kullanılan yöntemlerden biridir. Doğada kesin olarak tanımlanamayan birçok olayın bulanık mantık yardımıyla çözümleri mümkün hale gelmiştir. Uygulama alanının geniş olması ve birçok problemin çözümünde başarılı sonuçların elde edilmesi bu yönteme olan ilgiyi arttırmıştır.Bulanık mantığın jeofizik alanındaki uygulamaları da giderek artmaktadır. Özellikle sismik, elektromanyetik ve özdirenç gibi yöntemlerin ters çözümünde ayrıca parametre tayini ve ön kestirim gibi uygulamalarda kullanılmaktadır. Bu çalışmada bulanık mantığın günümüze kadar olan jeofizik uygulamaları derlenmiş ve yaygın olarak kullanım amaçları özetlenmeye çalışılmıştır. Batı Anadolu deprem katalog verilerinin Uyarlanabilir Yapay Sinir-Bulanık Mantık Çıkarım Sistemi (Adaptive Neurofuzzy Inference System) (UYBÇS) ile değerlendirilmesi üzerine örnek bir çalışmaya yer verilmiştir. With the effect of advancing technology, Fuzzy logic has become one of the most common methods used in solving problems during the recent years. Solutions of the many ill defined/unidentified events in nature/earth are made possible by means of fuzzy logic. Wide ranges of applications and obtaining successful results are caused the increasing interest on this method.Applications of Fuzzy logic on Geophysics are also increasing day by day. It is used on particularly inversion of seismic, electromagnetic and resistivity data, prediction of some physical parameters and estimation studies. The aim of this study is to compile the articles which are about Fuzzy logic application on geophysics and to summarize its intended purpose. Analyzing of the Earthquake data of Western Anatolia Using with Adaptive Neurofuzzy Inference System, is given an example of this method as a seismological application

Permeability Prediction and Diagenesis in Tight Carbonates Using Machine Learning Techniques

Machine learning techniques have found their way into many problems in geoscience but have not been used significantly in the analysis of tight rocks. We present a case study testing the effectiveness of artificial neural networks and genetic algorithms for the prediction of permeability in tight carbonate rocks. The dataset consists of 130 core plugs from the Portland Formation in southern England, all of which have measurements of Klinkenberg-corrected permeability, helium porosity, characteristic pore throat diameter, and formation resistivity. Permeability has been predicted using genetic algorithms and artificial neural networks, as well as seven conventional ‘benchmark’ models with which the machine learning techniques have been compared. The genetic algorithm technique has provided a new empirical equation that fits the measured permeability better than any of the seven conventional benchmark models. However, the artificial neural network technique provided the best overall prediction method, quantified by the lowest root-mean-square error (RMSE) and highest coefficient of determination value (R2). The lowest RMSE from the conventional permeability equations was from the RGPZ equation, which predicted the test dataset with an RMSE of 0.458, while the highest RMSE came from the Berg equation, with an RMSE of 2.368. By comparison, the RMSE for the genetic algorithm and artificial neural network methods were 0.433 and 0.38, respectively. We attribute the better performance of machine learning techniques over conventional approaches to their enhanced capability to model the connectivity of pore microstructures caused by codependent and competing diagenetic processes. We also provide a qualitative model for the poroperm characteristics of tight carbonate rocks modified by each of eight diagenetic processes. We conclude that, for tight carbonate reservoirs, both machine learning techniques predict permeability more reliably and more accurately than conventional models and may be capable of distinguishing quantitatively between pore microstructures caused by different diagenetic processes