An optimization method for the assisted history matching (AHM) process using the gradient boosting approach

Melnikov, Max Y.; Shishaev, G.; Matveev, Ivan; Eremyan, G.; Demyanov, Vasily; Bukhanov, N.; Belozerov, Boris

doi:10.3997/2214-4609.202075026

Cited by 1 publication

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“…The problems of forecasting the effectiveness of oil recovery enhancement technologies, production stimulation and well stimulation are considered in References [36][37][38][39][40][41][42][43][44], where the following algorithms are mainly used: Shallow and Deep Artificial Neural Networks (ANN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Dimension Reduction using Principal Component Analysis (PCA); the model's correlation coefficients vary significantly between 0.6 and 0.9.…”

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confidence: 99%

The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

2022

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This paper aims to study the applicability of machine-learning algorithms, specifically neural networks, for forecasting the effectiveness of Improved recovery methods. Radial jet drilling is the case operation in this study. Understanding changes in reservoir flow properties and their effect on liquid flow rate is essential to evaluate the radial jet drilling effectiveness. Therefore, liquid flow rate after radial jet drilling is the target variable, while geological and process parameters have been taken as features. The effect of various network parameters on learning quality has been assessed. As a result, conclusions on the applicability of neural networks to evaluate the radial jet drilling potential of wells in various geological conditions of carbonate reservoirs have been made.

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