In ultra-deep well operations, severe lateral vibration of drill string is a major factor in tool failure and decreased drilling efficiency. To investigate the vibration mechanisms and identify effective mitigation approaches, a dynamic model for lateral vibration in ultra-deep well drill strings was established using Cosserat geometrically exact beam theory. The model systematically examined the effects of rotational speed, WOB, and stabilizer position and size on the vibration behavior. Key findings were validated against downhole measurement data from ultra-deep wells. Additionally, two control strategies leveraging modal competition and transverse wave disturbance were proposed. Results indicate that the bottom hole assembly (BHA) is particularly prone to intense lateral vibrations, with its vibrational modes governed by rotational speed and WOB. When the WOB is below the critical buckling load, increasing either the rotational speed or WOB promotes backward whirling of the BHA, thereby intensifying the vibration severity and bending stress. Conversely, when the WOB exceeds the critical buckling load, the system transitions into a buckling–whirling competition mode, resulting in a significant reduction in the vibration intensity and bending stress. This trend was reasonably verified through field data. Artificially inducing this low-risk modal competition by adjusting the WOB and rotational speed can effectively reduce the probability of drill string failure. The motion of stabilizers shifts from forward whirling to backward whirling as the diameter decreases, which considerably alters the vibration-propagation patterns. The vibration-damping effects of both full-gauge and under-gauge stabilizers initially increase and then decrease as their installation position moves upward. Under-gauge stabilizers exhibit less consistent behavior under non-severe vibration conditions; nevertheless, they can suppress severe whirling by interfering with adjacent drill string vibrations through low-frequency transverse waves. They also demonstrate lower sensitivity to the installation position and enhance drill string safety through stress dispersion. Considering comprehensive vibration suppression, drill string integrity, and engineering applicability, installing under-gauge stabilizers can be a viable BHA optimization measure with significant practical value. This study provides a theoretical basis for vibration control in ultra-deep well drill strings, and the proposed strategy offers valuable insights for improving drilling efficiency and ensuring operational safety.

Rock cohesion strength and internal friction angle are the fundamental and important parameters of borehole stability and hydraulic fracturing research. The conventional method for obtaining the above rock strength properties is triaxial compression test standardized by the International Society of Rock Mechanics (ISRM). The rock mechanics parameters including compressive strength and elastic modulus can be obtained by applying a certain confining pressure to the specimen, and then gradually increasing the axial pressure until shear failure occurs in the rock sample. And because of the advantages of strong adaptability and accurate measurement, triaxial compression experiment method has been widely used. However, this method has high requirements for sample making and which will lead to rock sample breakage, so the sample can’t be reused again. Because it is difficult to make standard samples of complex rock mass, this method is not suitable in complex study. In this paper, based on the mechanical model of rock scratching with sharp and blunt cutters in plastic failure mode, and combined with Mol-coulomb criterion, the mechanical model of rock cohesion strength and internal friction angle were established by using the force of rock breaking with cutters. Then, with the continuous scratch test device, the influence of the order, location and depth of the sharp and blunt cutters on the rock breaking mechanical behavior of the cutters were evaluated, and the scratch parameter setting methods and scratch tests process for accurately obtaining the rock breaking stress curve of the cutters were proposed. The normal stress and shear stress were obtained by cutting the rock sample with sharp and blunt cutters, and then the rock mechanics parameters including cohesion strength and internal friction angle can be obtained by substituting them into the calculation model. Compared with the traditional triaxial compression test, the values of rock mechanical parameters measured by using the continuous scratch methods were consistent with those measured by the traditional triaxial compression test, and there was a certain proportion between them, which proveed that the new method is feasible. The new method requires simple sample preparation specifications, short experimental time, and can test rock strength parameters of complex rock. At the same time, the whole structure of rock sample will not be destroyed in the process of experiment, so the experiment has strong repeatability, which provides a new idea for the test of sample strength parameters before and after water rock interaction, and also provides a new method for the study of hydration.

Lost circulation is a common problem encountered in complex formation drilling engineering, which is characterized by frequent occurrence, randomness and persistence. Accurate prediction of potential thief zone before drilling is particularly important for safe drilling. The traditional analysis of lost circulation is focused on diagnosis while drilling and summary after drilling, mainly using the means of combining engineering data and field experience, which leads to the lag of analysis results and cannot effectively guide drilling engineering design before drilling. Based on seismic attributes and lost circulation engineering data, this paper extracted the seismic attributes of drilled wells on the basis of the selection of single wells with typical lost circulation characteristics, and selected the seismic attributes with strong correlation with lost circulation prediction by time-depth relationship and adopted random forest method to identify and select the seismic attributes with strong correlation with lost circulation prediction. Then, an ensemble learning model was established by using soft voting algorithm in machine learning method. The model integrates three sub-models named logistic regression, random forest and support vector machine, and realizes the nonlinear mapping relationship between multiple seismic attributes and lost circulation engineering data and the corresponding weight characterization. At the same time, the probability of lost circulation risk distribution driven by the fusion of seismic and engineering data is obtained, and the 3D spatial distribution prediction of pre-drilling lost circulation risk layer is realized. The results show that variance, time-frequency attenuation, sweet spot and root mean square amplitude have the highest correlation with lost circulation. Combining the above attributes can achieve more accurate lost circulation risk prediction. However, excessive addition of seismic attributes cannot significantly improve the prediction accuracy, on the contrary, it will increase the calculation cost. Compared with a single machine learning model, ensemble learning model can achieve better prediction results because it combines the advantages of multiple sub-models. The practical application results show that the accuracy of lost circulation risk prediction by using seismic attributes depends on the sampling rate of seismic data. The horizontal prediction resolution of the thief zone risk is about 25 m, and the vertical prediction resolution is about 6 m (2 ms). The prediction results show that the horizontal prediction is more reliable than the vertical prediction. However, due to the influence of time-depth relationship, the longitudinal prediction accuracy may be offset. This study provides a new way to predict pre-drilling lost circulation, which is of great significance to guide well location deployment, well trajectory optimization and safe drilling.

Wellbore stability is a key factor affecting safe and efficient drilling. At present, it is difficult to conduct real-time and accurate analysis of wellbore stability in related research. To address the current research shortcomings, this study proposes a real-time analysis model of wellbore stability integrating image recognition and an expert system, which mainly includes caving image segmentation and recognition, and a wellbore stability expert system. The caving image recognition proposes a new dynamic threshold segmentation method based on simple linear iterative clustering superpixel segmentation and visual geometry group 19-layer image classification. After completing the segmentation of the caving image, the geometric features of the caving are calculated, and the multi-source feature fusion GoogleNet model is established by integrating the geometric features with the convolution features extracted by GoogleNet to identify the caving types efficiently. After segmentation and recognition of caving images. The wellbore stability expert system uses the caving features to establish an expert system model to determine the mechanism of wellbore instability and provide reasonable solutions. Finally, the wellbore stability integrating image recognition and an expert system model was applied to a well in field production, accurately determining the mechanism of wellbore instability in real time and effectively solving the corresponding wellbore instability problem based on the measures provided by the model.

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering. Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction. However, most of the traditional deep learning models are less efficient to address generalization problems. To fill this technical gap, in this work, we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data. Specifically, the new model, named CGP-NN, consists of a novel parametric features extraction approach (1DCPP), a stacked multilayer gated recurrent model (multilayer GRU), and an adaptive physics-informed loss function. Through machine training, the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction. The CGP-NN model has the best generalization when the physics-related metric λ = 0.5. A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels. To validate the developed model and methodology, a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability. The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

The presence of micro-fractures in shale reservoirs is vital for economic production. While a number of models have been proposed to predict the propagation pressure of pre-existing micro-fractures, few models have considered capillary pressure, which may play a significant role in the presence of micro-fractures with nano-scale width. In this study, a new model was developed to predict the propagation pressure of micro-fractures. It is assumed that pre-existing micro-fractures are arbitrarily intersected with the propagated hydraulic fractures. The model was derived based upon linear elastic fracture mechanics under the condition of mode I fracture propagation coupled with capillary pressure. Furthermore, this paper also conducted sensitivity analyses to predict the micro-fracture propagation pressure as a function of the contact angle, surface tension and the width of micro-fracture. The results demonstrated that decreasing the contact angle reduces the propagation pressure of micro-fractures, implying that a hydrophilic system may yield a lower fracture propagation pressure compared with the hydrophobic counterpart. Moreover, for a hydrophilic system, further decreasing the contact angle shifts the propagation pressure to a negative value, implying that the capillary pressure may induce the propagation of micro-fractures without external fluid injection. The propagation pressure is also affected by the surface tension and the width of micro-fracture.