ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R²(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

The shallow unconsolidated stratum in the offshore oilfield is characterized by large porosity, low temperature, and weak formation and often faces problems such as low density and poor compressive strength of a cement slurry, among others, which pose serious challenges to construction. A high‐performance low‐density cement slurry system must be used for cementing to ensure the safety of subsequent drilling and mining on‐site and to reduce the cost of cement slurry for the efficient development of oil and gas fields. Based on these problems, according to the principle of particle gradation, a mixture with a high accumulation rate and low density composed of five types of mineral materials, i.e., artificial microbeads, floating beads, microsilicon, fly ash, and slag, has been developed through a large number of indoor experiments, and a set of low‐cost and low‐density cement slurry systems has been developed; these systems are suitable for the shallow loose formations of offshore oil fields. The cement slurry system meets the requirements of the cementing operation conducted under different temperatures and pressures. The density range is 1.4–1.7 g/cm³, which can be adjusted. The cement slurry is stable and exhibits good fluidity. The thickening time meets the requirements of cementing construction. Moreover, the compressive strength of the cement paste is high, and the compressive strength of cement paste is greater than 12 MPa for 24 h and 14 MPa for 48 h of curing at 50°C, which maximizes the economic benefits. The research results provide technical support for the safe and efficient development of offshore oil and gas fields.

Natural gas hydrate reservoirs, with shallow burial, poor cementation, and low strength, are prone to submarine landslides triggered by hydrate decomposition during extraction. Prior studies have inadequately considered factors such as the dynamic decomposition of hydrates during depressurization, and its impacts on the reservoir’s geomechanical properties. In this paper, a coupled thermal–hydraulic–mechanical–chemical mathematical model of hydrate decomposition is proposed, and the dynamic geomechanical response and the effect of hydrate decomposition on seafloor settlement and slope destabilization during the process of depressurization mining are analyzed by combining the strength discount method with the example of a hydrate-bearing seafloor slope in the Shenhu area. Furthermore, the study employs an orthogonal experimental design along with range and variance analysis to gauge the impact of critical factors (degree of hydrate decomposition, seawater depth, hydrate reservoir burial depth, hydrate reservoir thickness, and slope angle) on slope stability. The findings suggest that hydrate decomposition is non-uniform and is influenced by stratigraphic temperature gradients and gravity. In the region where hydrate decomposition occurs, the decrease of pore pressure leads to the increase of effective stress. Additionally, the decomposition of hydrates decreases the shear modulus of sediments, leading to deformation and reduced permeability in the affected area. Over a three-year period of depressurization mining, the significantly reduced safety factor increases the risk of landslides. Various factors play a role in the control of submarine slope stability, with slope inclination being the primary factor, followed by the degree of hydrate decomposition, reservoir thickness, burial depth, and seawater depth. Among these factors, hydrate burial depth and seawater depth have a positive correlation with submarine slope stability, while increases in other factors generally decrease stability. These research findings have important implications for the safe exploitation of slopes that contain hydrates.

Marine gas hydrate formations are characterized by considerable water depth, shallow subsea burial, loose strata, and low formation temperatures. Drilling in such formations is highly susceptible to hydrate dissociation, leading to gas invasion, wellbore instability, reservoir subsidence, and sand production, posing significant safety challenges. While previous studies have extensively explored multiphase flow dynamics between the formation and the wellbore during conventional oil and gas drilling, a clear understanding of wellbore stability under the unique conditions of gas hydrate formation drilling remains elusive. Considering the effect of gas hydrate decomposition on formation and reservoir frame deformation, a multi-field coupled mathematical model of seepage, heat transfer, phase transformation, and deformation of near-wellbore gas hydrate formation during drilling is established in this paper. Based on the well logging data of gas hydrate formation at SH2 station in the Shenhu Sea area, the finite element method is used to simulate the drilling conditions of 0.1 MPa differential pressure underbalance drilling with a borehole opening for 36 h. The study results demonstrate a significant tendency for wellbore instability during the drilling process in natural gas hydrate formations, largely due to the decomposition of hydrates. Failure along the minimum principal stress direction in the wellbore wall begins to manifest at around 24.55 h. This is accompanied by an increased displacement velocity of the wellbore wall towards the well axis in the maximum principal stress direction. By 28.07 h, plastic failure is observed around the entire circumference of the well, leading to wellbore collapse at 34.57 h. Throughout this process, the hydrate decomposition extends approximately 0.55 m, predominantly driven by temperature propagation. When hydrate decomposition is taken into account, the maximum equivalent plastic strain in the wellbore wall is found to increase by a factor of 2.1 compared to scenarios where it is not considered. These findings provide crucial insights for enhancing the safety of drilling operations in hydrate-bearing formations.

Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments.