Optimization of fracturing technology for unconventional dense oil reservoirs based on rock brittleness index.

The concept of volume fracturing has revolutionized the conventional limits of low permeability, expanded the effective resource space, and significantly enhanced oil well production in tight oil reservoir development. This paper elucidates the mechanism of volume fracturing technology for tight sandstone reservoirs by considering multiple factors such as the initiation range of multi-fractures, influence of far-well horizontal principal stress on fracture initiation and propagation, degree of natural fractures development, and mechanical parameters of reservoir rock. Through simulation based on the mechanical parameters of reservoir rock, a comparative analysis was conducted between the model-calculated rock fracture pressure value and measured data from fracturing construction wells in the study area. The results revealed that there was a discrepancy within 10% between the model calculations and actual data. By simulating the effects of different injection volumes of fracturing fluid, pumping rates, and perforation methods on the fracture geometry, optimal design parameters for volume fracturing technology were obtained. Additionally, we propose optimization ideas and suggestions for construction parameters applicable to field operations. The simulation results indicate that a minimum recommended fluid volume scale exceeding 1800 m3 is advised for the reservoir. Based on frictional calculations, it is recommended to have an on-site construction rate not less than 18.0 m3/min along with 36-48 holes/section for perforation purposes. The numerical simulation research presented in this paper provides a theoretical reference basis and practical guidance for the application of fracturing network technology in tight sandstone reservoirs.

ROP Prediction Method Based on PCA-Informer Modeling.

Increasing the rate of penetration (ROP) is an effective means to improve the drilling efficiency. At present, the efficiency and accuracy of intelligent prediction methods for the rate of penetration still need to be improved. To improve the efficiency and accuracy of rate of penetration prediction, this paper proposes a ROP prediction model based on Informer optimized by principal component analysis (PCA). We take the Taipei Basin block oilfield as an example. First, we use principal component analysis to extract data features, transforming the original data into low-dimensional feature data. Second, we use the PCA-optimized data to build an Informer model for predicting ROP. Finally, combined with actual data and using the recurrent neural network (RNN) and long short-term memory (LSTM) as baselines, we perform algorithm performance comparative analysis using root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2). The results show that the average MAE, RMSE, and R 2 of the PCA-Informer model are 9.402, 0.172, and 0.858, respectively. Compared with other methods, it has a larger R 2 and smaller RMSE and MAPE, indicating that this method significantly outperforms existing methods and provides a new solution to improve the rate of penetration in actual drilling operations.

Development and Characterization of Thermoresponsive Smart Self-Adaptive Chitosan-Based Polymer for Wellbore Plugging.

To meet the escalating demand for oil and gas exploration in microporous reservoirs, it has become increasingly crucial to develop high-performance plugging materials. Through free radical grafting polymerization technology, a carboxymethyl chitosan grafted poly (oligoethylene glycol) methyl ether methyl methacrylate acrylic acid copolymer (CCMMA) was successfully synthesized. The resulting CCMMA exhibited thermoresponsive self-assembling behavior. When the temperature was above its lower critical solution temperature (LCST), the nanomicelles began to aggregate, forming mesoporous aggregated structures. Additionally, the electrostatic repulsion of AA chains increased the value of LCST. By precisely adjusting the content of AA, the LCST of CCMMA could be raised from 84.7 to 122.9 °C. The rheology and filtration experiments revealed that when the temperature surpassed the switching point, CCMMA exhibited a noteworthy plugging effect on low-permeability cores. Furthermore, it could be partially released as the temperature decreased, exhibiting temperature-switchable and self-adaptive plugging properties. Meanwhile, CCMMA aggregates retained their reversibility, along with thermal thickening behavior in the pores. However, more detailed experiments and analysis are needed to validate these claims, such as a comprehensive study of the CCMMA copolymer's physical properties, its interaction with the reservoir environment, and its performance under various conditions. Additionally, further studies are required to optimize its synthesis process and improve its efficiency as a plugging material for oil and gas recovery in microporous reservoirs.