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.