Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs

Gholami, Raoof; Moradzadeh, Ali; Maleki, Shahoo; Amiri, Saman; Hanachi, Javid

doi:10.1016/j.petrol.2014.09.007

Cited by 57 publications

(15 citation statements)

References 24 publications

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“…Technique Source of data [25] ANN + Multiple Linear Regression + Multiple Nonlinear Regression Venture Gas Field Offshore Canada [29] ANN + Regression Analysis Southwest Iranian Oil Field [28] ANN Sarawak Foreland Basin [30] ANN + Wavelet Theory Not disclosed [45] FN Middle Eastern Oil Well [43] ELM Middle Eastern Oil Well [42] ANN + FL Hassi Oil Field Algeria [51] SVM South Pars Field Iran [24] ANN + GRNN Gramy Greek Field in Western Virginia [27] ANN Uinta Basin [52] ANN Iranian Oil Field [34] ANN Abu Dhabi [32] ANN Unknown [33] ANN Asman Oil Field in Southern Iran [26] ANN North Sea [35] ANN, MLP, MLR, SVR Middle Jurassic Shaximiao, Western Sichuan Basin China [36] ANN Middle Eastern Oil Well [39] ANN Persian Gulf Iranian Offshore [37] ANN Middle Eastern Oil Well [40] MLP, RBF, GRNN Kangan and Dallan Formation (South Pars Field Iran) [41] Evolving ANN + GA Mansuri Bangestan (Ahwaz) Iran [38] MLP + SVM + CANFIS Mesaverde Tight Gas, Washakie Basin, USA [15] FL Sarvak and Asmari Formation (Iran) [46] Fuzzy c-means + ANN Kangan Iran (Offshore Gas Field) [47] FL Middle Eastern Oil Well [48] FL Mesozoic Strata Gaoqing [49] FL North Sea and Ula Field South West of Norway [50] T2FL Middle Eastern Oil Well to improve the generalization of ELM, Olatunji et al [43] introduced T2FL to handle uncertainties. As ELM is fast, with better generalization, and avoid local minima, the combined model is used to predict permeability in the Middle Eastern reservoir.…”

Section: Referencesmentioning

confidence: 99%

A Comprehensive Review of Soft Computing Models for Permeability Prediction

Almutairi

2021

IEEE Access

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Section: Referencesmentioning

confidence: 99%

A Comprehensive Review of Soft Computing Models for Permeability Prediction

Almutairi

2021

IEEE Access

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“…Two methods including Haykin's and Trenn's for determining hidden neurons was used and data splits of 66% and 80% were considered. Gholami et al [43] used a genetic algorithm (GA) for selecting the most suitable input logs and performed permeability (mD) prediction using support vector regression (SVR) and relevance vector regression (RVR). RVR yielded more accurate results compared to SVR for the tested wells.…”

Section: And Da In Reservoir Performance Optimizationmentioning

confidence: 99%

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

2020

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In the mid-19th century, the first oil well was drilled, which marked the beginning of the world oil economy. This era embarked on the phase of evolution and development of society in every aspect, with huge dependence on petroleum products, which subsequently led to tremendous growth in terms of technological advancements in the exploration and production activities over time. With the existing petroleum fields approaching toward depletion, the global need for energy resources is increasing simultaneously. This continuously rising demand has led us to move to complex environments for oil and gas exploration and production activities. Moreover, the real-time monitoring of reservoirs and well operations produces enormous data, which require efficient processing and analysis tools for decisionmaking regarding field development and asset management. [1,2] Subsequently, the need for optimum facilities design, [3] transportation [4] and refining [5] activities at reduced cost has also grown. Several attempts [6-9] are also being made to develop unconventional fields commercially based on technoeconomic viability. The implementation of computer-based techniques has proven to be promising toward many challenging problems for various oilfield operations. Several researchers have shown that the machine learning (ML) and data analytics (DA) approaches hold promising solutions toward the operational challenges being encountered at present in the industry and can efficiently help in resolving the issues related to interpretation and analysis of large datasets. [10-15] Nikravesh and Aminzadeh [16] have shown the use of neural networks (NNs) and fuzzy logic for mining petroleum data. Li and Li [17] combined a NN and cluster analysis to put forth a predictive model for identifying complex lithology. Fath et al. [18] proposed a novel approach for bubble point pressure prediction using an NN model with reservoir temperature, solution gas oil ratio (GOR), oil gravity, and gas specific gravity as input attributes. Ahmadi and Bahadori [19] showed the use of a supervised learning algorithm to determine the well placement and conning occurrence in horizontal wells. Maucec et al. [20] performed data mining and ML on well stimulation data for enhancing the prediction capabilities. Gupta et al. [21] applied DA for safeguarding real-time electrical submersible pumping operations. Cadei et al. [22] forecasted operational upsets using advanced analytics in an upstream production system. Dongxiao et al. [23] presented in their research that long short-term memory (LSTM), cascaded LSTM, and a fully connected NN can be utilized for generating synthetic well logs by supplementing the missing logging input data. Ghorbani et al. [24] proposed an artificial Intelligence (AI)based approach to predict the flow rate of oil from an orifice

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“…This has been done using di↵erent techniques, with GAs being used to optimise the parameters of other predictors such as the weights in ANNs [19], or the coe cients in FL and SVMs [1,25]. A slightly di↵erent application example is to use the GA as an optimiser to find the best logs for prediction of permeability [16], that is, to select the data that will render the best estimator.…”

Section: Genetic Algorithm Approachesmentioning

confidence: 99%

An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

Lopes

Nikhalat-Jahromi

Jorge

2016

Proceedings of the Ninth International C* Conference on Computer Science &Amp; Software Engineering - C3S2E '16

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The Oil and Gas Exploration & Production (E&P) field deals with high-dimensional heterogeneous data, collected at di↵erent stages of the E&P activities from various sources. Over the years di↵erent soft-computing algorithms have been proposed for data-driven oil and gas applications. The most popular by far are Artificial Neural Networks, but there are applications of Fuzzy Logic systems, Support Vector Machines, and Evolutionary Algorithms (EAs) as well. This article provides an overview of the applications of EAs in the oil and gas E&P industry. The relevant literature is reviewed and categorised, showing an increasing interest amongst the geoscience community. CCS Concepts •Applied computing ! Earth and atmospheric sciences; •Information systems ! Data analytics; Data mining;

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Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs

Cited by 57 publications

References 24 publications

A Comprehensive Review of Soft Computing Models for Permeability Prediction

A Comprehensive Review of Soft Computing Models for Permeability Prediction

Identifying Applications of Machine Learning and Data Analytics Based Approaches for Optimization of Upstream Petroleum Operations

An Overview of Evolutionary Computing for Interpretation in the Oil and Gas Industry

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