Journal of Petroleum Exploration and Production Technology最新文献

Horizontal drilling and multistage hydraulic fracturing have seen widespread application in shale formations during the past decade, leading to significant economic productivity gains through the creation of extensive fracture surfaces. The determination of the ideal cluster spacing in shale gas wells is contingent upon the unique geological and formation characteristics. Generally, reducing the spacing between clusters has the potential to augment gas recovery, albeit at the expense of higher drilling and completion costs, as well as the influence of stress shadows on fracture propagation. This study introduces an integrated methodology designed to explore the impact of cluster interference on well performance. Commencing with a fracture propagation model accommodating stress shadow effects for an equivalent slurry volume injection, analytical rate transient analysis (RTA) was amalgamated with reservoir numerical simulation to compute the effective fracture surface area (A_ca.) for hydrocarbon production. The correlation between the effective fracture surface area determined by RTA and the actual stimulated fracture area (A_ca.) derived from numerical simulations was established in relation to cluster spacing. The findings of this research reveal that wells featuring a greater number of stages and tighter cluster spacing tend to exhibit elevated cluster interference, resulting in a lower effective-to-actual fracture surface area ratio and heightened stress shadow effects impeding fracture propagation. A cluster spacing of 33 feet with six clusters per stage emerges as the optimal choice at formation permeability of 0.00005 md that decreased to 18 ft at formation permeability of 0.00001 md. A_Ce either stabilizes or decreases above the optimal value, suggesting that more clusters would not have a major impact on increasing the effective stimulated area. Allowing 20% interference, regardless of the permeability of the formation, maximized cumulative production while preventing thief zones and excessive cluster interference. The insights gained from this study will serve as a valuable resource for completion and reservoir engineers, enabling them to fine-tune cluster spacing to maximize well revenue in the dynamic landscape of shale gas extraction.

Drilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency.

During oil production, the reservoir pressure declines, causing changes in the hydrocarbon components. To ensure better separation of produced phases, separator dimensions should also be adjusted. It is not possible to change the dimensions of the separator during production. Therefore, to improve the separation of the phases, the level of the separator needs to be adjusted. An intelligent system is required to ensure that the liquid level is maintained at the desired level for optimal phase separation during changes in reservoir pressure. In this study, a novel correlation is presented to measure the desired liquid level using new separator pressures. For this purpose, an intelligent system was built in the laboratory and tested in different operational conditions. The intelligent system effectively maintained the desired liquid level of the separator through a new correlation technique. The system accomplished this by acquiring new separator pressure readings collected by installed sensors. This approach helped mitigate the negative effects of the slug flow regime and minimized issues such as foam formation and over-flushing of the separator. It could achieve a 99.1% separation efficiency between gas and liquid phases. This was possible during liquid and gas flow rates ranging from 0 to 2.35 and 8–17 m³/h, respectively. The system could operate under bubble, stratified, plug, and slug flow regimes. Then the intelligent model obtained from lab experiments was integrated into the production model for the southern Iranian oil field. The smart model increased oil production by 13% and prevented the separator from over-flushing in 840 days.

Relative permeability (RP) curves which provide fundamental insights into porous media flow behavior serve as critical parameters in reservoir engineering and numerical simulation studies. However, obtaining accurate RP curves remains a challenge due to expensive experimental costs, core contamination, measurement errors, and other factors. To address this issue, an innovative approach using deep learning strategy is proposed for the prediction of rock sample RP curves directly from mercury injection capillary pressure (MICP) measurements which include the mercury injection curve, mercury withdrawal curve, and pore size distribution. To capture the distinct characteristics of different rock samples' MICP curves effectively, the Gramian Angular Field (GAF) based graph transformation method is introduced for mapping the curves into richly informative image forms. Subsequently, these 2D images are combined into three-channel red, green, blue (RGB) images and fed into a Convolutional Long Short-Term Memory (ConvLSTM) model within our established self-supervised learning framework. Simultaneously the dependencies and evolutionary sequences among image samples are captured through the limited MICP-RP samples and self-supervised learning framework. After that, a highly generalized RP curve calculation proxy framework based on deep learning called RPCDL is constructed by the autonomously generated nearly infinite training samples. The remarkable performance of the proposed method is verified with the experimental data from rock samples in the X oilfield. When applied to 37 small-sample data spaces for the prediction of 10 test samples, the average relative error is 3.6%, which demonstrates the effectiveness of our approach in mapping MICP experimental results to corresponding RP curves. Moreover, the comparison study against traditional CNN and LSTM illustrated the great performance of the RPCDL method in the prediction of both S_o and S_w lines in oil–water RP curves. To this end, this method offers an intelligent and robust means for efficiently estimating RP curves in various reservoir engineering scenarios without costly experiments.

Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves.

Acoustic impedance is the product of the density of a material and the speed at which an acoustic wave travels through it. Understanding this relationship is essential because low acoustic impedance values are closely associated with high porosity, facilitating the accumulation of more hydrocarbons. In this study, we estimate the acoustic impedance based on nine different inputs of seismic attributes in addition to depth and two-way travel time using three supervised machine learning models, namely extra tree regression (ETR), random forest regression, and a multilayer perceptron regression algorithm using the scikit-learn library. Our results show that the R² of multilayer perceptron regression is 0.85, which is close to what has been reported in recent studies. However, the ETR method outperformed those reported in the literature in terms of the mean absolute error, mean squared error, and root-mean-squared error. The novelty of this study lies in achieving more accurate predictions of acoustic impedance for exploration.

Sequestering CO₂ in depleted oil reservoirs provides one of the most appealing measures to reduce greenhouse gases (GHG) concentration in the atmosphere. The remaining liquids after enhanced oil recovery (EOR) processes, including residual oil and remaining water, lead to the main challenges to this approach. How to effectively evacuate a depleted oil reservoir by recovering not only residual oil but also remaining water is a critical consideration for this type of CO₂ sequestration. This paper presents conceptual investigations concerning the methods which effectively evacuate depleted oil reservoirs from both the displacement efficiency and the sweep efficiency points of view. To improve the displacement efficiency, surfactant slug and solvent slug injection was examined using a core scale numerical model. Investigations regarding improving sweep efficiency, such as horizontal well pattern infilling and foam injection, were carried out based on a typical row well pattern. Simulation results showed that surfactant slug which modified the relative permeability and capillary pressure remarkably reduced both residual oil saturation and remaining water saturation. A CO₂ slug injected before surfactant slug can help improve the oil recovery. Solvent enriched CO₂ slug also remarkably reduced the residual oil saturation to as low as 2%. Horizontal well pattern infilling had great advantage for thick or inclined reservoirs, and foam slug injection greatly improved CO₂ storage capacity in thin reservoirs by improving the sweep efficiency. Maximum mobility reduction (MRF) is the most important parameter to maximize the storage capacity and the benefit. The variation of CO₂ storage capacity along with CO₂ slug size. Larger foam slug size will play a better storage performance. The conceptual simulation investigations confirmed that depleted oil reservoirs can be effectively evacuated for CO₂ storage. Depleted oil reservoirs with maximum evacuation are the best candidates for CO₂ sequestrations.

Pseudo threshold pressure gradient (PTPG) exists in the propped fractured reservoir, but its nonlinear flow law remains unclear. The effects of the mineral composition of shale and microstructure of fracturing fluid on PTPG were analyzed by X-ray diffraction and liquid nitrogen quick-freezing method. The results demonstrate that a proppant with a large particle size is more likely to form an effective flow channel and reduce liquid flow resistance, thus decreasing PTPG and increasing conductivity. The polymer fracturing fluid with rectangular microstructures significantly increased the PTPG supporting the fractured core. Experimental results show that the PTPG of the resin-coated sand-supported core in the fracturing fluid with a concentration of 1.2% is 245 times higher than that in the fracturing fluid with a concentration of 0.1% when the confining pressure is 5 MPa. Wetting hysteresis and the Jamin effect are responsible for the rise of PTPG in two-phase flow. The equivalent fracture width shows a good power function relationship with the PTPG. Thus, this study further explains the nonlinear flow behavior of reservoirs with fully propped fractures.

The installation of sand screens in open-hole completions in the wellbore is crucial for managing sand production. The main reason for using standalone screens in open-hole completions is their relatively reduced operational complexity compared to other sand control technologies. However, directly applying the screen to the bottom of the hole can lead to an incorrect screen type selection, resulting in an unreliable sand control method. To address this issue, a sand retention test is conducted to evaluate the performance of a standalone screen before field installation. Nevertheless, current sand retention test setups encounter several challenges. These include difficulties in identifying minimum retention requirements, interpreting results in the context of field conditions, and replicating field-specific parameters. The existing sand retention test introduces uncertainties, such as inaccurately replicating field requirements, inconsistent selection of wetting fluids, flow rates, and channel formation, leading to variations in the choice of the optimal screen using this test. In response to these challenges, this study aims to review the sand retention test and propose an improved sand retention method to overcome these problems. The focus of this article is to provide an in-depth analysis of previous sand retention test setups, their contributions to characterizing sand screens, and the parameters utilized in determining test outcomes. Additionally, this review outlines a procedure to investigate the impact of different particle sizes on screen erosion. Key findings emphasize the importance of using high-quality materials, proper screen design to resist damage and erosion, achieving acceptable natural packing behind the screen, and considering factors such as geology, wellbore conditions, and installation techniques. The analysis reveals that a high quantity of finer and poorly sorted sand increases sand production. The study recommends performing a sand pack test closer to reservoir conditions for better evaluation. Premium sand screens demonstrate the highest retention capacity, followed by metal mesh and wire-wrapped screens. Additionally, geotextiles show potential for enhancing sand retention, and screen design affects erosion resistance and service life.

The integrity of shale gas wells is crucial in ensuring safety and efficiency throughout the development process. Such integrity spans the entire process of drilling and fracturing horizontal wells and is an essential indicator for ensuring safe and stable production throughout the lifespan of the well. This study investigates methods for assessing the integrity of shale gas wells by employing the analytic hierarchy process combined with experimental data to establish evaluation criteria and weights. The assessment is carried out specifically on shale gas wells in Changning Block. Results indicate that the integrity of these shale gas wells is influenced by various factors, such as drilling and fracturing processes. Moreover, the integrity assessment of indicators such as oil layer casing/technical casing, liquid carrying capacity, and tube column deformation is relatively low, indicating a need for enhanced monitoring and management. The comprehensive evaluation results indicate that, overall, the integrity rating of shale gas wells is generally considered “common,” but some potential safety hazards still remain that require timely attention and resolution. Case analysis reveals varying levels of integrity risks in shale gas wells. Case 1’s score of 93.51 warrants attention but is still deemed generally safe. However, Case 2’s score of 73.89 indicates a disaster level, emphasizing urgent intervention needs. Critical factors such as pressure, cementation quality, and corrosion demand proactive management for safe, sustainable operations.