Application of machine learning algorithms in classification the flow units of the Kazhdumi reservoir in one of the oil fields in southwest of Iran

Abstract

By determining the hydraulic flow units (HFUs) in the reservoir rock and examining the distribution of porosity and permeability variables, it is possible to identify areas with suitable reservoir quality. In conventional methods, HFUs are determined using core data. This is while considering the non-continuity of the core data along the well, there is a great uncertainty in generalizing their results to the entire depth of the reservoir. Therefore, using related wireline logs as continuous data and using artificial intelligence methods can be an acceptable alternative. In this study, first, the number of HFUs was determined using conventional methods including Winland R35, flow zone index, discrete rock type and k-means. After that, by using petrophysical logs and using machine learning algorithms including support vector machine (SVM), artificial neural network (ANN), LogitBoost (LB), random forest (RF), and logistic regression (LR), HFUs have been determined. The innovation of this article is the use of different intelligent methods in determining the HFUs and comparing these methods with each other in such a way that instead of using only two parameters of porosity and permeability, different data obtained from wireline logging are used. This increases the accuracy and speed of reaching the solution and is the main application of the methodology introduced in this study. Mentioned algorithms are compared with accuracy, and the results show that SVM, ANN, RF, LB, and LR with 90.46%, 88.12%, 91.87%, 94.84%, and 91.56% accuracy classified the HFUs respectively.

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

“…This number is transferred to the output layer using the transfer function in the hidden layer. Some percent of the data are also used for comparison 1,4,51,55,63,[85][86][87] .…”

“…Jafarzadeh, Kadkhodaie, Ahmad, Kadkhodaie and Karimi 49 investigated the distribution of reservoir facies by using the available data to identify areas that are prone to be considered in the production and development plans of the field in terms of their storage and fluid flow capacity. Eventually, the experimental results illustrated that the Adaptive multi-resolution graph-based clustering algorithm for electrifying analysis also outperformed the original MRGC method on clustering and propagation prediction with higher efficiency and stability 31,50,51 . Recently, the electrifies predicted using the MRGC approach to generate rock mechanical properties such as Young's modulus, Poisson's ratio, unconfirmed compressive strength, and internal friction coefficient [52][53][54] .…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023). Salavati et al (2023) used hydraulic flow units, multi-resolution graphbased clustering, and fuzzy c-mean clustering methods to determine rock types.…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Forbes Inskip et al, 2020;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023). Salavati et al (2023) used hydraulic flow units, multi-resolution graph-based clustering, and fuzzy c-mean clustering methods to determine rock types.…”