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.

In response to the unclear drilling resistance characteristics of rocks in the Ji’yang Depression, low drilling efficiency of PDC drill bits, and difficulties in drill bit selection, this study selected rock samples from different depths in the area for indoor drilling resistance analysis testing. Based on logging data, a prediction model was established for drilling resistance characteristics parameters of the strata in the area, and a graph of drilling resistance characteristic parameters of the rocks in the area was drawn. The study showed that the uniaxial compressive strength of the strata rocks was 50–110 MPa, with a hardness of 500–1300 MPa, a plasticity coefficient ranging from 1 to 2, a rock drillability grade of 8–20, and an abrasiveness index of 5–20. Combining the analysis of on-site drilling bit failures, PDC drill bits adapted to the strata in the area were selected, and the mechanical drilling speed of the selected bits reached 12.58 m/h, successfully drilling through the target layer. The above research results are of guiding significance for understanding the reasons for the difficulty of drilling into the Jiyang Depression strata and for improving mechanical drilling speed and drill bit selection in this area.

Wellbore leakage mostly occurs in structurally developed fractured formations. Analyzing the real-time leakage rate during the drilling process plays an important role in identifying the leakage mechanism and its rules on-site. Based on the principles of fluid mechanics and using Herschel-Bulkley (H-B) drilling fluid, by reasonably simplifying the drilling fluid performance parameters, fracture roughness characteristic parameters, pressure difference between the wellbore and formation, and the radial extension length of drilling fluid, the radial leakage model is improved to improve the calculation accuracy. Using the Euler format in numerical analysis to solve the model and with the help of numerical analysis software, the radial leakage law of this flow pattern in the fractures is obtained. The results show that the deformation coefficient of the fracture index, fracture aperture, pressure difference, leakage rate, and cumulative leakage rate are positively correlated. The larger the curvature of the fracture, the rougher the fracture, and the smaller the leakage rate and cumulative leakage rate. The larger the consistency coefficient of the drilling fluid, the greater the additional resistance between the fractures, and the smaller the leakage rate and cumulative leakage rate. As the extending length of the fracture increases, the invasion of drilling fluid decreases, the leakage rate slows down, and eventually reaches zero, with the maximum cumulative leakage rate.

Hydraulic fracturing is a crucial technology for enhancing the recovery of oil and gas from unconventional reservoirs. Accurately describing fracture morphology is essential for accurately predicting production dynamics. This article proposes a new fracture inversion model based on dynamic data-driven methods, which is different from the conventional linear elastic fracture mechanics model. This method eliminates the need to consider complex mechanical mechanisms, resulting in faster simulation speeds. In the model, the fracture morphology is constrained by combining microseismic data and fracturing construction data, and the fracture tip propagation domain is introduced to characterize the multi-directionality of fracture propagation. The simulated fracture exhibits a multi-branch fracture network morphology, aligning more closely with geological understanding. In addition, the influence of microseismic signal intensity on the direction of fracture propagation is considered in this study. The general stochastic approximation (GSA) algorithm is employed to optimize the direction of fracture propagation. The proposed method is applied to both the single-stage fracturing model and the whole well fracturing model. The research findings indicate that in the single-stage fracturing model, the inverted fracture morphology aligns closely with the microseismic data, with a fitting rate of the fracturing construction curve exceeding 95%, and a microseismic data fitting rate exceeding 93%. In the whole well fracturing model, a total of 18 sections were inverted. The fitting rate between the overall fracture morphology and the microseismic data reached 90%. The simulation only took 5 minutes, demonstrating high computational efficiency and meeting the needs of large-scale engineering fracture simulation. This method can effectively support geological modeling and production dynamic prediction.

In response to the unclear rock properties of the formations in the Tazhong-Tabei block of the Tarim Basin and the difficulties in selecting drill bits, indoor rock drillability tests were conducted under high temperature and high-pressure conditions. The tests analyzed the changes in the drillability grade values of roller cone bits and PDC bits under the coupling effects of confining pressure and temperature. The research indicates that the drillability grade values of the rocks in this formation range between 4 and 6. Additionally, the drillability grade values of PDC bits are lower than those of roller cone bits. As confining pressure increases, the drillability grade values of the rocks also increase significantly when the confining pressure exceeds 30 MPa. Conversely, as the temperature rises, the drillability grade values of the rocks decrease substantially when the temperature exceeds 140 °C. A drillability analysis model for roller cone bits and PDC bits was established using finite element simulation software. The simulated drillability grade values of the rocks showed a discrepancy of less than 10% compared to the indoor test data, proving the accuracy and reliability of the indoor test data. Based on the rock drillability test results, the use of PDC bits is recommended for this formation. The optimized PDC bits successfully drilled through the target formation. These research findings are of guiding significance for accurately understanding the physical and mechanical properties of the formation rocks in the Tazhong-Tabei block and for the optimal selection of drill bits for this area.

Both domestic and international scholars have conducted in-depth research on wellbore stability issues. They have established various empirical models, analytical models, and numerical simulation methods. However, there is relatively little research on the impact of the uncertainty of input parameters on wellbore stability, and the understanding of this aspect remains unclear. To address this, this paper introduces a probability distribution method. It is based on a wellbore stability mechanical analytical model and, using reliability theory, establishes a method for evaluating wellbore instability risks. By employing the Monte Carlo random simulation method, this study investigates the sensitivity of input parameters to wellbore stability, clarifying that ground stress is the main controlling factor affecting wellbore stability. Combining the analysis of the “felt layer” ground stress profile, this study utilizes two-dimensional simulation experiments to validate the accurate determination of ground stress magnitude in wellbore stability analysis. It also examines the impact of reducing its uncertainty. The results show that this approach significantly reduces the risk of wellbore instability, addressing the challenging issue of identifying wellbore instability in the Qiu Dong Depression’s “felt layer” within the TH Basin.