Recent advances in the application of computational intelligence techniques in oil and gas reservoir characterisation: a comparative study

Abstract

A comparative study of the predictive capabilities of recent advances in computational intelligence (CI) is presented. This study utilised the machine learning paradigm to evaluate the CI techniques while applying them to the prediction of porosity and permeability of heterogeneous petroleum reservoirs using six diverse well data sets. Porosity and permeability are the major petroleum reservoir properties that serve as indicators of reservoir quality and quantity. The results showed that the performance of support vector machines (SVM) and functional networks (FN) is competitively better than that of Type-2 fuzzy logic system (T2FLS) in terms of correlation coefficient. With execution time, FN and SVM were faster than T2FLS, which took the most time for both training and testing. The results also demonstrated the capability of SVM to handle small data sets. This work will assist artificial intelligence practitioners to determine the most appropriate technique to use especially in conditions of limited amount of data and low processing power.Keywords: machine learning; computational intelligence; support vector machines; functional networks; Type-2 fuzzy logic system; petroleum reservoir characterisation 1. Background Support vector machines (SVM), functional networks (FN) and Type-2 fuzzy logic system (T2FLS) are the three popular computational intelligence (CI) techniques that have individually featured in many successful applications published in recent times (

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

“…98.49% of all articles were published in English. Twenty-one other languages appeared, the most frequent of which were Chinese (59), Spanish (51), French (51), German (41), and Portuguese (39). The numbers of publications categorized by year were reported in Table 1.…”

“…As a booming field, AI mainly undeniably relies on numerous methods, such as SVM, SI, CI, and PSO. Albeit these methods do not have a large number of applications, many scholars suggested these methods will have more and more applications, and become more important in AI [41][42][43][44]. Table 6.…”

Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques

“…CI can address such complications with relative ease . Some of the domains of the petroleum engineering in which CI techniques brought new innovations include porosity-permeability predictions (Abdulraheem et al 2007;El-Sebakhy et al 2012;Nooruddin et al 2013;Helmy et al 2013;Anifowose et al 2013Anifowose et al , 2014Anifowose et al , 2017, hydraulic flow unit identification (Shujath Ali et al 2013), rock mechanical parameters estimation (Yang and Rosenbaum 2002;Sonmez et al 2004;Abdulraheem et al 2009;Cevik et al 2011;Tariq et al 2018b), missing petrophysical well logs estimation (Tariq et al 2019), welltesting parameters estimation (Artun 2017;Bazargan and Adibifard 2017), asphaltene and wax precipitation predictions (Rezaian et al 2010;Adeyemi and Sulaimon 2012;Fattahi et al 2015;Alimohammadi et al 2017), water saturation prediction (Adebayo et al 2015;Bageri et al 2015;Baziar et al 2016Baziar et al , 2018Khan et al 2018), gas compressibility factor (Mohagheghian et al 2015;Tariq and Mahmoud 2019), oil well drilling rate of penetration optimization (Gidh et al 2012), and many other oil and gas applications (Ashena et al 2010;Jahanandish et al 2011;Asoodeh 2013;Rammay and Abdulraheem 2016).…”

“…This technique is originated from the learning principles of biological neurons found in human body (Graves et al 2009). Recent advances in the mathematics of neural network and its ability to solve complex and nonlinear problems have gained wide recognition in the petroleum industry (Anifowose et al 2014).…”

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

“…Computational intelligence (CI) has positively impacted the oil and gas industry especially the reservoir characterization and modeling process in the recent time (AlBulushi et al 2009;Dutta and Gupta 2010;Asadisaghandi and Tahmasebi 2011;Al-Marhoun et al 2012;Barros and Andrade 2013;Anifowose et al 2014a). This positive impact resulted from the successful applications of various CI techniques such as artificial neural networks (ANNs), functional networks (FNs), fuzzy logic (FL), generalized regression neural network, support vector machines (SVMs), and radial basis function.…”