Acid Fracturing Productivity Model for Naturally Fractured Carbonate Reservoirs

Ugursal, Assiya; Schwalbert, Mateus Palharini; Zhu, Ding; Hill, A.D.

doi:10.2118/191433-18ihft-ms

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…The contribution of the developed model over the aforementioned ones , is the ability of the fracture to propagate and activate NFs. As the HF propagates, new girds are generated to account for the increase in HF size and the activation of NFs.…”

Section: Methodsmentioning

confidence: 99%

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

2021

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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.

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Section: Methodsmentioning

confidence: 99%

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

2021

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“…Alhubail et al (2017) developed an acid transport model using the finite element method to improve treatment efficiency [15]. Ugursal et al (2018) presented a new model for predicting acid fracturing performance in naturally fractured carbonate reservoirs and investigated the effect of natural fractures on acid fracturing stimulation efficiency [16]. Dang et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Pumping Schedule Optimization in Acid Fracturing Treatment by Unified Fracture Design

Lotfi¹,

Hosseini²,

Aftabi³

et al. 2021

Preprint

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Acid fracturing simulation is used widely to optimize carbonate reservoirs and improve acid fracturing treatment performance. In this study, a method was used to minimize the risk of the acid fracturing treatment. First, optimal fracture geometry parameters with UFD methods are calculated. After that, design components change as long as fracture geometry parameters reach their optimal values. The results showed a high flow rate needed to achieve optimal fracture geometry parameters with increasing acid volume. Sensitivity analysis was performed on controllable and reservoir parameters. It observed that a high flow rate should be applied for a low fluid viscosity to achieve the optimization goals. Straight acid reaches optimal conditions at a high flow rate and low volume. These conditions for retarded acids appear only at a low flow rate and high volume. The study of the acid concentration for gelled acid showed that as it increased, the flow rate and volume increased. Besides, for low permeability formation, a large fracture half-length and small fracture width are desirable. In this case, a higher flow rate will be required. The sensitivity analysis showed that the optimum flow rate and acid volume increase and decrease for the high Young's modulus. The effect of closure stress was also investigated and observed for a sample with high closure stress, low flow rate, and high acid volume are required.

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“…Alhubail et al developed an acid transport model using the finite element method to improve treatment efficiency [15]. Ugursal et al presented a new model for predicting acid fracturing performance in naturally fractured carbonate reservoirs and investigated the effect of natural fractures on acid fracturing stimulation efficiency [16]. Acid filtration in a fractured reservoir to investigate acid filtration in natural fractures, wormholes, and matrix on acid injection distance was modeled [17].…”

Section: Introductionmentioning

confidence: 99%

Pumping Schedule Optimization in Acid Fracturing Treatment by Unified Fracture Design

et al. 2021

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Acid fracturing simulation has been widely used to improve well performance in carbonate reservoirs. In this study, a computational method is presented to optimize acid fracturing treatments. First, fracture geometry parameters are calculated using unified fracture design methods. Then, the controllable design parameters are iterated till the fracture geometry parameters reach their optimal values. The results show higher flow rates are required to achieve optimal fracture geometry parameters with larger acid volumes. Detailed sensitivity analyses are performed on controllable and reservoir parameters. It shows that higher flow rates should be applied for fluids with lower viscosity. Straight acid reaches optimal conditions at higher flow rates and lower volumes. These conditions for retarded acids appear to be only at lower flow rates and higher volumes. The study of the acid concentration for gelled acids shows that both flow rate and volume increase as the concentration increases. For the formation with lower permeability, a higher flow rate is required to achieve the desired larger fracture half-length and smaller fracture width. Further investigations also show that the formation with higher Young’s modulus requires decreasing the acid volume and increasing the optimal flow rate, while the formation with higher closure stress requires increasing the acid volume and decreasing the flow rate.

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Acid Fracturing Productivity Model for Naturally Fractured Carbonate Reservoirs

Cited by 12 publications

References 9 publications

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

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

Pumping Schedule Optimization in Acid Fracturing Treatment by Unified Fracture Design

Pumping Schedule Optimization in Acid Fracturing Treatment by Unified Fracture Design

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