Application of machine learning and artificial intelligence in oil and gas industry

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.

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Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data

A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

Achieving important and effective reservoir parameters requires a lot of time and cost, and also achieving these devices is sometimes not possible. In this research, a dataset including 565 datapoints collected from published articles have been used. The input data for forecasting oil formation volume factor (OFVF) were solution gas oil ratio (Rs), gas specific gravity (γg), API gravity (API0) (or oil density γo), and temperature (T). We have tried to introduce two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms to predict this parameter and compare their results after extraction. After essential investigations in this study, the results show that MLP-ABC gives the best accuracy for predicting OFVF. For MLP-ABC model OFVF prediction accuracy in terms of RMSE T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest.

https://jpme.journals.ekb.eg/article_169885_a07ee2fd8c3194431b77e7b42df72aeb.pdf

Omid Hazbeh

Papers

Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm