An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs

Abstract

Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young’s modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production

“…A number of studies confirmed the effectiveness of ML models in application to oil recovery factor estimation [14][15][16]. Several other studies have reported the successful application of ML models to estimate the effects of hydraulic fracturing [17][18][19][20]. Kornkosky et al applied multivariate linear regression to estimate the waterflooding effect [11].…”

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. − …”

“…12,13 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). 14,15 Also, the treatment conditions such as acid concentration, injection rate, pressure, and temperature can affect the treatment performance. 16−18 Several studies were conducted to optimize the efficiency of acid fracturing treatments, and experimental and simulation approaches were used.…”

Pumping Schedule Optimization in Acid Fracturing Treatment by Unified Fracture Design

“…Al-Ameri et al (2020) employed FRACPRO software to optimize acid fracturing design for a tight carbonate reservoir to investigate the impact of acid injection stages and acid fluids types on the fracture geometry parameters and the fracture conductivity [25]. Hassan et al (2021) presented a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques [26].…”