Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization

Anifowose, Fatai; Abdulraheem, Abdulazeez

doi:10.1016/j.jngse.2011.05.002

Cited by 97 publications

(40 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, in their later attempts (Anifowose and Abdulraheem 2011;Anifowose et al 2013Anifowose et al , 2014b, the same authors (mentioned above) focused on rather simpler methodologies to combine the hybrid models following the Occam Razor's principle of simplicity (Jefferys and Berger 1991). Due to the simplicity of the newly proposed design of the hybrid models, the contributions of each component became clear.…”

Section: Hybrid Intelligent Systems In Petroleum Reservoir Characterimentioning

confidence: 99%

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

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

Self Cite

View full text Add to dashboard Cite

Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.

show abstract

Section: Hybrid Intelligent Systems In Petroleum Reservoir Characterimentioning

confidence: 99%

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

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…invertible) function. More detailed description of FN along with the functional equations derivations and simplification can be found in Castillo, Gutierrez, et al (2001), , and Anifowose and Abdulraheem (2011).…”

Section: Functional Networkmentioning

confidence: 99%

“…More details on T2FLS can be found in Karnik and Mendel (1999), Mendel (2003), Maqsood and Adwait (2000), Chen, Li, Harrison, and Zhang (2007), Anifowose and Abdulraheem (2011), Olatunji et al (2011a, 2011b.…”

Section: Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

“…One of the relatively new areas where these techniques have been applied includes oil and gas reservoir characterisation and modelling. Oil and gas reservoir characterisation is a process for describing, in qualitative and quantitative terms, various reservoir properties in spatial variability by using available field data (Anifowose & Abdulraheem, 2011). It plays a very important role in modern reservoir management, making sound reservoir decisions, reducing uncertainties and hence, improving the reliability of reservoir predictions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Anifowose

Adeniye

Abdulraheem

2014

Journal of Experimental & Theoretical Artificial Intelligen

Self Cite

View full text Add to dashboard Cite

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 (

show abstract

“…Over the last few decades, a number of studies have investigated the application of a range of decision support and artificial intelligence techniques and technologies in candidate well selection. These range from decision support systems using multivariate nonlinear regression [4][5][6] to neural networks [7][8][9][10], analytical hierarchy process (AHP) [11][12][13][14][15][16] and fuzzy logic [10,[17][18][19][20]. Each of these approaches carries with it a set of advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

et al. 2017

View full text Add to dashboard Cite

Abstract:The selection of appropriate wells for hydraulic fracturing is one of the most important decisions faced by oilfield engineers. It has significant implications for the future development of an oilfield in terms of its productivity and economics. In this study, we developed a fuzzy model for well selection that combines the major objective criteria with the subjective judgments of decision makers. This was done by fusing the analytic hierarchy process (AHP) method, grey theory and an advanced version of fuzzy logic theory (FLT). The AHP component was used to identify the relevant criteria involved in selecting wells for hydraulic fracturing. Grey theory was used to determine the relative importance of those criteria. Then a fuzzy expert system was applied to fuzzily process the aggregated inputs using a Type-2 fuzzy logic system. This undertakes approximate reasoning and generates recommendations for candidate wells. These techniques and technologies were hybridized by using an intercommunication job-sharing method that integrates human judgment. The proposed method was tested on data from an oilfield in Western China and finally the most appropriate candidate wells for hydraulic fracturing were ranked in order of their projected output after fracturing.

show abstract

Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization

Cited by 97 publications

References 22 publications

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

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

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

Hybridising Human Judgment, AHP, Grey Theory, and Fuzzy Expert Systems for Candidate Well Selection in Fractured Reservoirs

Contact Info

Product

Resources

About