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.

Development and Characterization of CO2-Responsive Intelligent Polymer Sealant.

In this study, we present an innovative intelligent polymer sealant designed to mitigate CO2 leakage during underground geological storage (CCUS). This sealant is formulated by cross-linking CO2-responsive polymers, specifically acrylamide (AM) and N-[3-(dimethylamino) propyl] methacrylamide (DMAPMA), with polyethylenimine (PEI) serving as the cross-linking agent. The polymer sealant's characteristics were systematically investigated, varying the CO2-responsive polymer content (1.5 wt %) and PEI content (0.1-0.6 wt %). A comprehensive analysis encompassing the rheological properties, thermal behavior, conductivity, and microstructures was conducted. Experimental results indicate that the polymer sealant exhibits excellent injectability, rapid response kinetics, thermal stability, and robust mechanical strength. Upon encountering CO2, the polymer system undergoes a transition from sol to gel state, forming a surface-smooth, uniformly porous three-dimensional (3D) network skeleton structure. Remarkably, the gel's modulus remains relatively unaffected by the shear frequency. Core fluid displacement experiments demonstrated a substantial sealing efficiency of 73.6% for CO2 and an impressive subsequent injection water sealing rate of 96.2%, underscoring its superior sealing and migration performance. In conclusion, the proposed CO2-responsive gel sealant exhibits an exceptional potential for successful utilization in CCUS operations. This advancement introduces a novel avenue to enhance the effectiveness of CO2-responsive gel sealants, thereby contributing to the advancement of CO2 leakage mitigation strategies in geological storage scenarios.

An Advanced Long Short-Term Memory (LSTM) Neural Network Method for Predicting Rate of Penetration (ROP).

Rate of penetration (ROP) is an essential factor in drilling optimization and reducing the drilling cycle. Most of the traditional ROP prediction methods are based on building physical model and single intelligent algorithms, and the efficiency and accuracy of these prediction methods are very low. With the development of artificial intelligence, high-performance algorithms make reliable prediction possible from the data perspective. To improve ROP prediction efficiency and accuracy, this paper presents a method based on particle swarm algorithm for optimization of long short-term memory (LSTM) neural networks. In this paper, we consider the Tuha Shengbei block oilfield as an example. First, the Pearson correlation coefficient is used to measure the correlation between the characteristics and eight parameters are screened out, namely, the depth of the well, gamma, formation density, pore pressure, well diameter, drilling time, displacement, and drilling fluid density. Second, the PSO algorithm is employed to optimize the super-parameters in the construction of the LSTM model to the predict ROP. Third, we assessed model performance using the determination coefficient (R 2), root mean square error (RMSE), and mean absolute percentage error (MAPE). The evaluation results show that the optimized LSTM model achieves an R 2 of 0.978 and RMSE and MAPE are 0.287 and 12.862, respectively, hence overperforming the existing methods. The average accuracy of the optimized LSTM model is also improved by 44.2%, indicating that the prediction accuracy of the optimized model is higher. This proposed method can help to drill engineers and decision makers to better plan the drilling operation scheme and reduce the drilling cycle.

Study on the Model and Law for Radial Leakage of Drilling Fluid in Fractured Formations.

The economic loss caused by fracture leakage accounts for 90% of all leakage costs; thus, it is necessary to find the factors that affect the leakage and to study the leakage laws of fractured strata. The advantage of this article is that we introduced fracture index deformation and fracture tortuosity parameters to characterize fracture roughness and fracture characteristic parameters using the logging data analysis method. To explore the mechanism of leakage in essence, this paper, based on fluid mechanics, improves the radial leakage model by adopting the Herschel-Bulkey (H-B) flow type drilling fluid with high calculation accuracy and comprehensively considering the factors such as drilling fluid performance parameters, fracture roughness characteristic parameters, pressure difference between the wellbore and formation, and radial extension length of the drilling fluid. The advantage of the model is that it is solved in an absolutely stable backward Euler difference format. The numerical simulation is carried out by MATLAB. The simulation results revealed that the leakage rate increased as the fracture index deformation coefficient and the fracture opening increased. The leakage rate also increased as the fracture tortuosity parameters decreased and as the fracture smoothened. However, the leakage rate decreased as the drilling fluid consistency coefficient increased. Drilling fluid dynamic shear force had a minor effect on the leakage rate. The higher the pressure difference between the wellbore and the formation, the higher the leakage rate. As the drilling fluid intrusion depth increased, the leakage rate decreased until it reached 0. Two parameters were mainly controlled in order to control the degree of leakage: differential pressure and fracture static width, which has important guiding for adjusting the drilling fluid density and predicting leaks in the field. The solution method of the model in this paper has a certain reference value for the solution of other models in the future. The conclusion can provide reference for numerical simulation, laboratory test, and field application in the future.