Data-Driven Acid Fracture Conductivity Correlations Honoring Different Mineralogy and Etching Patterns

Abstract

Acid-fracturing operations are mainly applied in tight carbonate formations to create a highly conductive path. Estimating the conductivity of a hydraulic fracture is essential for predicting the fractured well productivity. Several models were developed previously to estimate the conductivity of acid-fractured rocks. In this research, machine learning methods were applied to 560 acid fracture experimental datapoints to develop several conductivity correlations that honor the rock types and etching patterns. Developing one universal correlation often results in significant error. To develop conductivity correlations, various data preprocessing methods were applied to remove the outliers and failed experiments. Features that did not contribute to precise predictions were removed through regularization. A machine learning classifier was built to predict the etching pattern based on the input data. We generated a multivariate linear regression model and compared it with other models generated through ridge regression. In addition to that, artificial neural network-based model was proposed to predict the fracture conductivity of several carbonate rocks such as dolomite, chalk, and limestone. The performance of the developed models was assessed using well-known metrics such as precision, accuracy, mean squared error, recall, and correlation coefficients. Cross-validation was also employed to assure accuracy and avoid overfitting. The classifier accuracy was 93%, while the regression model resulted in a relatively high correlation coefficient.

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A systematic review of data science and machine learning applications to the oil and gas industry

“…The authors showed that the model accuracy was improved as compared to Akbari et al's model. Desouky et al (2020aDesouky et al ( , 2020b) utilized more than 500 data points to generate a more accurate acid fracture conductivity correlation that considers rock type and etching pattern.…”

Comparative Study of Fracture Conductivity in Various Carbonate Rocks Treated with GLDA Chelating Agent and HCl Acid

“…Stimulation treatments are applied to increase the productivity of tight hydrocarbon-bearing formations. − Several techniques are used, such as acidizing, hydraulic fracturing, or combined approach. − Acids can be injected at high pressure to induce rough fracture surfaces during hydraulic fractures; such treatment is defined as acid fracturing . The ultimate goal of acid fracturing treatments is creating rough fracture surfaces by injecting acids at high pressure. , Increasing the roughness of fracture surfaces can increase fracture conductivity which is defined as the fracture width multiplied by its permeability. , Various factors can affect the performance of acid fracturing operations, including reservoir properties (e.g., reservoir permeability and natural fractures) and fracture properties (i.e., fracture size and conductivity). , Also, the treatment conditions such as acid concentration, injection rate, pressure, and temperature can affect the treatment performance. − …”