Search Results (3)

The development of unconventional oil and gas resources highly relies on hydraulic fracturing technology, and the fracturing effect directly affects the level of oil and gas recovery. Carrying out fracturing evaluation is the main way to understand the fracturing effect. However, the current fracturing evaluation methods are usually carried out after the completion of fracturing operations, making it difficult to achieve real-time monitoring and dynamic regulation of the fracturing process. In order to solve this problem, an intelligent prediction method for fracture propagation based on the attention mechanism and Long Short-Term Memory (LSTM) neural network was proposed to improve the fracturing effect. Firstly, the GOHFER software was used to simulate the fracturing process to generate 12,000 groups of fracture geometric parameters. Then, through parameter sensitivity analysis, the key factors affecting fracture geometric parameters are identified. Next, the time-series data generated during the fracturing process were collected. Missing values were filled using the K-nearest neighbor algorithm. Outliers were identified by applying the 3-sigma method. Features were combined through the binomial feature transformation method. The wavelet transform method was adopted to extract the time-series features of the data. Subsequently, an LSTM model integrated with an attention mechanism was constructed, and it was trained using the fracture geometric parameters generated by GOHFER software, forming a surrogate model for fracture propagation. Finally, the surrogate model was applied to an actual fracturing well in Block Ma 2 of the Mabei Oilfield to verify the model performance. The results show that by correlating the pumping process with the fracture propagation process, the model achieves the prediction of changes in fracture geometric parameters and Stimulated Reservoir Volume (SRV) throughout the entire fracturing process. The model’s prediction accuracy exceeds 75%, and its response time is less than 0.1 s, which is more than 1000 times faster than that of GOHFER software. The model can accurately capture the dynamic propagation of fractures during fracturing operations, providing reliable guidance and decision-making basis for on-site fracturing operations. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

Hydraulic fracturing can be utilized to extract trapped hydrocarbon where integrated fracture networks do not exist for sufficient production. In this work, design parameters of a hydraulic acid fracturing of a tight carbonate reservoir in the Middle East were optimized. The effect of optimized hydraulic fracturing on production performance and rate was investigated. Using the petrophysical well logs, formation integrity tests, core data the Mechanical Earth Model (MEM) of the tight carbonate reservoir was created, which resulted in rock mechanical properties and in-situ stresses. The other required parameters for fracturing design were either measured or found from empirical correlations. Following a candidate selection of suitable layers for fracturing, the input parameters were loaded in GOHFER software to design and optimize the fracturing job. Finally, the production forecast was performed and compared with current conditions. The injection parameters (flow rate, total volume, and number of stages) of the fracturing fluid (composed of guar and CMHPG and polymer with 15% HCL acid) were optimized to reach optimum resultant fracture geometry. Finally, optimized injection parameters were found at the injection flow rate of 18 barrels per minute, total injection volume of 90 K-gal, and three stages of injection. Using the optimal injection parameters, the optimized fracture geometrical sizes were determined: the fracture half-length (L_f): 148 m (486 ft), fracture height (H_f) of 64 m (210 ft) and fracture width (W_f) of 0.0962 in. Finally, the effect of this stimulation method on future production performance was investigated. The well production rate showed an increase from 840 STB/Day (before fracturing) to 1270 STB/Day (post fracturing). This study contributes to the practical design and optimization of hydraulic fracturing in the tight carbonate formation of the investigated oilfield and the other potential fields in the region. The results showed that this stimulation method can efficiently improve production performance from reservoir formation. Full article