Prediction of Porosity and Permeability Alteration Based on Machine Learning Algorithms

Abstract

The objective of this work is to study the applicability of various Machine Learning algorithms for prediction of some rock properties which geoscientists usually define due to special lab analysis. We demonstrate that these special properties can be predicted only basing on routine core analysis (RCA) data. To validate the approach core samples from the reservoir with soluble rock matrix components (salts) were tested within 100+ laboratory experiments. The challenge of the experiments was to characterize the rate of salts in cores and alteration of porosity and permeability after reservoir desalination due to drilling mud or water injection. For these three measured characteristics, we developed the relevant predictive models, which were based on the results of RCA and data on coring depth and top and bottom depths of productive horizons. To select the most accurate Machine Learning algorithm a comparative analysis has been performed. It was shown that different algorithms work better in different models. However, two hidden layers Neural network has demonstrated the best predictive ability and generalizability for all three rock characteristics jointly. The other algorithms, such as Support Vector Machine and Linear Regression, also worked well on the dataset, but in particular cases. Overall, the applied approach allows predicting the alteration of porosity and permeability during desalination in porous rocks and also evaluating salt concentration without direct measurements in a laboratory. This work also shows that developed approaches could be applied for prediction of other rock properties (residual brine and oil saturations, relative permeability, capillary pressure, and others), which laboratory measurements are time-consuming and expensive.Keywords machine learning · routine and special core analysis · reservoir properties · salted formations · porosity and permeability alteration

Comparison of permeability predictions on cemented sandstones with physics-based and machine learning approaches

“…This is, to our knowledge, the first study using SHAP values to interpret machine learning results in predicting permeability. Erofeev, et al (2019) used gradient boosting regressors to predict permeability, but only used F scores to find the most important parameters and did not attempt to determine the functional dependence for each feature. Other studies that have used machine learning to predict permeability but not interpreted their models,…”

Predicting Permeability Based on Core Analysis

“…Examples include prediction of permeability of gas reservoirs using well logs and core data [9] [22], identifying drilling sweet-spots for gas hydrate reservoirs without pre-existing well logs [15] [10], rock texture image classification using support vector machines [21], predicting permeability during acidizing [11] and predicting the optimal rate of penetration during drilling [12]. The work closest to ours is the study by Erofeev et al [5] on the Chayandinskoye oil and gas condensate field in Russia. However, that study was limited to a single field and used desalination instead of drilling mud application during core analysis.…”

Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

“…The applications of deep learning (DL) and machine learning (ML) in the petroleum industry have gained more concern [19], particularly in forecasting oil production [20,21], forecasting of pressure-volume-temperature (PVT) properties [22,23], optimizing well placement and oil production [24,25], the prediction of reservoir petrophysical properties, including porosity and permeability [26,27], and oil spill detection [28]. Deep learning has been incorporated into the petroleum industry with the remarkable development of deep learning algorithms, enabling overcoming troublesome concerns in oilfields [21].…”