Permeability Estimation in Petroleum Reservoir by Meta-heuristics: An Overview

Mohebbi, Ali; Kaydani, Hossein

doi:10.1007/978-3-319-16531-8_10

Cited by 5 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include, but are not limited to, the following issues: prognostication of permeability [27,28], gas properties [29,30], wind power prediction [31], building cooling load prediction [32], application to economic issues [33] or issues related to air and land transport [34,35]. The nonlinear characteristics of network neurons have proved very successful in transforming the input data (explanatory variables) to approximate the output value (the dependent variable), which makes them highly effective compared to traditional regression analysis methods, e.g., [36][37][38]. Regression problems can be solved using a multilayer perceptron (MLP), radial basis networks (RBN), general regression neural networks (GRNN), and linear networks [39,40].…”

Section: Introductionmentioning

confidence: 99%

MLP-Based Model for Estimation of Methane Seam Pressure

2021

View full text Add to dashboard Cite

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

MLP-Based Model for Estimation of Methane Seam Pressure

2021

View full text Add to dashboard Cite

show abstract

“…If reservoir permeability can be accurately estimated, this is conducive to reservoir evaluation and production optimization, thereby decreasing production cost. However, due to the heterogeneity of reservoirs and their complex stratigraphic structure, it is a challenge to predict reservoir permeability accurately [1,2]. Currently, conventional reservoir permeability prediction methods include core analysis, well test analysis and the empirical formula.…”

Section: Introductionmentioning

confidence: 99%

Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data

Hu¹,

Pan²,

Kong³

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

Permeability estimation plays an important role in reservoir evaluation and hydrocarbon development, etc. Traditional physical model-based methods have problems with being time consuming and high cost. The applications of machine learning are currently becoming more and more extensive, however, there are still several limitations to previous machine learning-based permeability estimation methods, such as a limited number of samples, a requirement of prior knowledge, and some parameters needing to be calculated indirectly. In this paper, a hybrid reservoir permeability prediction approach, which is based on a certain scale of permeability dataset, embedded feature selection (EFS) and a light gradient boosting machine (LightGBM), is proposed. First, EFS is used to select features from the raw dataset. Then a LightGBM is adopted to predict the permeability. The influence of feature selection threshold, the base learners' number and dataset size on prediction results is also investigated. In addition, different feature selections and prediction models are compared. The proposed hybrid approach is also verified on other datasets. The experimental results show that the proposed approach can effectively predict the reservoir permeability based on limited direct logging data.

show abstract