Journal of Petroleum Exploration and Production Technology最新文献

The method of segmented hydraulic fracturing in the coal seam roof has proven to be an efficient technique for coalbed methane exploitation. However, the behavior of hydraulic fractures in multilayer formations with significant differences in mechanical properties is still unclear. This paper studied the variation in hydraulic fracture width at the coal-rock interface by employing experimental method with a true triaxial hydraulic fracturing experimental system and numerical simulation method. Results revealed that the hydraulic fracture more likely to expanded along the coal-rock interface instead of break through it with the small horizontal stress difference and low flow rate injection condition. And improving the injection flow rate lager than a critical value, the hydraulic fracture tends to break through the coal-rock interface. Hydraulic fractures in both mudstone and coal beds exhibited a trend of increasing and then decreasing of fracture width at the interface. Since the strength of the coal seam was lower compared to that of the mudstone, maintaining high pressure was no longer necessary when the hydraulic fracture crossed the interface and entered the coal seam, leading to a reduction in fracture width within the mudstone. During the later stages of fracturing, the entry of proppant into the coal seam became challenging, resulting in a phenomenon characterized by excessive fluid but insufficient sand. The time required for the fracture width to traverse the proppant was found to be inversely proportional to the difference in horizontal ground stress and the flow rate of the fracturing fluid. And it was directly proportional to the modulus of elasticity, permeability of the coal seam, and interface strength. The interface strength has the greatest influence on the width of hydraulic fractures. In conclusion, this study provides valuable insights into the behavior of hydraulic fractures in multilayer formations with varying mechanical properties. The findings contribute to a better understanding of the factors affecting hydraulic fracture width within coal seams, which can ultimately enhance the efficiency of coalbed methane exploitation.

The demand for fuel from unconventional sources is increasing all over the world, however, there are still special and strict regulations regarding the methods of enhanced oil recovery as well as the content of the oil produced, including the amount of sulfur. In-situ combustion (ISC) is an attractive thermal method to enhance oil recovery and in-situ upgrading process. In this work, copper (II) oleate and copper (II) stearate were used for the oxidation of extra heavy oil with high sulfur content in the ISC process using a self-designed porous medium thermo-effect cell (PMTEC) and visual combustion tube. Using PMTEC the catalytic performances of the synthesized oil-soluble copper (II) oleate and copper (II) stearate and kinetic parameters such as activation energy using Ozawa-Flynn-Wall method were studied. The X-ray diffraction (XRD) and high-resolution field emission scanning electron microscopy were used to examine the characteristics of in-situ synthesized CuO nanoparticles during oxidation. As shown, the presence of oil-soluble copper (II) stearate and copper (II) oleate reduced oil viscosity from 9964 to 8000 and 6090 mPa˙s, respectively. Following ISC process in porous media in the presence of copper (II) oleate, the high sulfur extra heavy oil upgraded, and its sulfur content decreased from 10.33 to 6.79%. Additionally, SARA analysis revealed that asphaltene and resin content decreased in the presence of oil-soluble catalysts. During the oxidation reaction, homogeneous catalyst decomposed into nanoparticles, and heterogeneous catalyst is distributed uniformly in porous media and played an active role in the catalytic process. It should be noticed that, these kind of oil-soluble catalysts can be novel and highly potential candidates for initiation and oxidation of extra heavy oil in order to decrease the viscosity, enhanced oil recovery and production of the upgraded oil.

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In sedimentary basins, deep-seated overpressure conditions are frequently encountered. However, the precise origins of these overpressure conditions and the assessment of their formation times have long presented challenges. Previous studies have primarily relied on qualitative approaches to investigate overpressure origins, leading to substantial uncertainties in their findings. Based on theories such as the effective stress law, disequilibrium compaction, equilibrium depth, and nested fluid trapping containers in this paper, a new quantitative methodology is introduced for identifying the disequilibrium-compaction-induced origins of overpressure conditions. Additionally, the formation times of overpressure can be also estimated by nested fluid trapping container theory. This methodology is successfully applied to the northwestern Bozhong subbasin in the Bohai Bay Basin, China. The results indicate that the overpressure within the Dongying Formation of the northwestern Bozhong subbasin is primarily attributed to the disequilibrium compaction of mudstone, because the disequilibrium compaction of mudstone accounts for over 90% of the pressure in sandstone. Furthermore, the overpressure system in this area is not singular but comprises multiple nested relative fluid trapping containers. The application of nested fluid trapping container theory allows for an estimation of the overpressure’s formation time, although further validation of these estimates is required. It should be noted that the method proposed in this paper is particularly suited for sedimentary basins with relatively weak tectonic activity.

Warning and controlling the water invasion in water-driving reservoirs is significant because water invasion will seriously hamper well productivity and gas recovery. Unfortunately, there are few comprehensive methods to control water invasion. First, we establish and verify a water invasion model of reservoir scale. Then, a new workflow for warning and controlling the water invasion is proposed using the numerical simulation method. The workflow first judges the water invasion characteristics, determines the water invasion index based on the production data, and then controls the water invasion by finding and closing the perforation layer of serious water production. Finally, the optimal water control scheme is obtained by comparing water and gas production. The results show that the accuracy of the geological reserves of the established water invasion model is 99% and has a good pressure fitting result. The early warning chart for the gas reservoir in the west of Amu Darya B area is drawn, including the early warning pressure and the level 1, level 2, and level 3 early warning water–gas ratio, which is convenient for field application. For the water-driving wells west of area B, the early warning value of the water–gas ratio increases with the increase of gas production rate during fixed production and decreases with the increase of bottom hole pressure during constant pressure production. Closing the harmful perforation from the water-finding study will significantly reduce the water while retaining the gas production. After water control technology, water production decreased by 90.9%, while gas production decreased by only 9.7%.

The coupled analysis of multi-field heat and mass transfer in geothermal reservoirs is a pivotal concern within the realm of geothermal rock exploitation. It holds significant implications for the assessment of thermal energy capacity and the formulation of reservoir optimization strategies in the context of geothermal rock resources. Parameters governing production, along with fracture network characteristics (such as injection well temperature, injection well pressure, fracture width, and fracture network density), exert an influence on enhanced geothermal systems (EGS) heat production. In this study, aiming to comprehend the dynamic heat generation of EGS during prolonged exploitation, a coupling of various fields including permeation within the rock formations of geothermal reservoirs and the deformation of these rocks was achieved. In this study, we formulated the governing equations for the temperature field, stress field, and permeability field within the geothermal reservoir rock. Subsequently, we conducted numerical simulations to investigate the heat transfer process in an enhanced geothermal system. We analyzed the effects of injection well temperature, injection well pressure, primary fracture width, and secondary fracture density on the temperature distribution within the reservoir and the thermal power output of the production well. The research findings underscore that ill-conceived exploitation schemes markedly accelerate the thermal breakthrough rate of production wells, resulting in a diminished rate of geothermal resource extraction from the geothermal reservoir rock. Variations in influent well temperature and secondary fracture density exhibit an approximately linear impact on the output from production wells. Crucially, injection well pressure and primary fracture width emerge as pivotal factors influencing reservoir output response, with excessive widening of primary fractures leading to premature thermal breakthrough in production wells.

Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R² correlation coefficient. The R² correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability.

Accurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS.

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The optimization of drilling parameters is crucial for resolving the drilling problems in low-pressure and leaky formations using the annulus aerated dual gradient drilling technology. However, the previous studies have mostly focused on engineering applications and wellbore fluid flow models, with less emphasis on parameter optimization. This paper combines the wellbore multiphase flow model with genetic algorithms for the first time, proposing a key parameter optimization method for annulus aerated dual gradient drilling based on genetic algorithms. The study investigates the impact of selection operators on the performance of genetic algorithms and compares genetic algorithms with PSO algorithm and SAA. The results indicate that the convergence and stability of genetic algorithms can be improved by enhancing the selection operators. Compared to the gas–liquid ratio parameter optimization method, the IRSGA optimization method reduces the cost coefficient by 36.46%. Through comparative analysis of different optimization methods, the IRSGA demonstrates over 95% accuracy in large-scale computations. The research findings contribute to the optimization of parameters design under low-cost conditions and are of significant importance for promoting the use of this technology to address the serious issue of lost circulation in drilling technology.

Overpressure is always considered as a severe problem in the oil industry. Besides creating life risks through serious accidents while drilling, failure to correctly identify high-pressure intervals causes a significant increase in costs and prolongs the drilling process. Kazhdumi Formation in the Abadan Plain is considered as a high-pressure formation in several wells. Various reasons can cause overpressure problem. Hydrocarbon generation is one cause of abnormal pressure in source rocks. Understanding hydrocarbon generation potential can be a helpful approach since the Kazhdumi Formation is considered as a probable source rock in this area. In this paper, in order to better understand the problem of abnormal pressure in Kazhdumi Formation, geochemical concepts and tools have been applied. To that way, 1D petroleum system modeling of five wells was done, and the thermal maturity level of Kazhdumi Formation was determined and then, compared with drilling records. The results indicate that in wells where this formation has sufficient organic matter and has reached an early mature level, there is an abnormal pressure problem. Otherwise, this formation does not show abnormal pressure. Also, geochemical data are not available in all drilled wells, which makes impossible the assessing of hydrocarbon generation role. Therefore, petrophysical well logs (sonic (DT), neutron (NPHI), density (RHOB), spectral gamma ray (SGR), and resistivity (RES)) as well as 137 sets of geochemical data belonging to 13 wells from 7 oilfields in the Abadan Plain were used to predict geochemical indicators. Using artificial neural networks, geochemical data of a well in Abadan Plain were predicted. This selected well has a high-pressure problem in Kazhdumi Formation, but no geochemical data are available in this well. The results of predicted geochemical data show that the high-pressure phenomenon in this well may also be due to hydrocarbon generation. The precise understanding of the abnormal pressure, resulting from hydrocarbons generation, requires comprehensive studies and a full investigation of the studied area. However, the results of this paper help to predict approximately the behavior of the source rocks before drilling. Acquiring this overview will aid in reducing drilling hazards and costs.

Spontaneous water imbibition into matrix blocks can be a significant oil recovery mechanism in fractured reservoirs. Many enhanced oil recovery methods, such as injection of modified salinity brine, are proposed for improving spontaneous imbibition efficacy. Many scaling equations are developed in the literature to predict spontaneous imbibition oil recovery. However, almost none of them included the impact of the diversity in ionic composition of injected and connate brines and the blending/interaction of a low salinity imbibing brine with a higher salinity connate brine. In this research, these two issues are included to propose new scaling equations for the scaling of spontaneous imbibition oil recovery by modified salinity imbibing brines. This study uses experimental data of the spontaneous imbibition of modified salinity brines into oil-saturated rock samples with different lithologies containing an irreducible high salinity connate brine. The collected tests from the literature were performed at high temperatures and on aged altered wettability cores. The results of 110 available spontaneous imbibition laboratory experiments (85, 12 and 13 tests on chalks, dolomites and sandstones, respectively) are gathered. This research initially shows the poor ability of three selected convenient scaling equations from the literature to scale imbibition recovery by modified salinity brine. Then, our newly proposed technique to find the scaling equation for spontaneous imbibition recovery by modified salinity brine, during the abovementioned conditions in limestones (Bassir et al. in J Pet Explor Prod Technol 13(1): 79–99, 2023. https://doi.org/10.1007/s13202-022-01537-7) is used in chalks, dolomites and sandstones to develop the three new scaling equations. Finally, a new general equation to scale imbibition recovery by modified salinity brine for all four lithologies is presented. Moreover, for each of the four datasets (chalk, dolomite, sandstone and all the four lithologies), the scaled data by the new equations is matched by two mathematical expressions based on the Aronofsky et al. model and the Fries and Dreyer model. These mathematical expressions can be used to develop transfer functions in reservoir simulators for a more accurate prediction of oil recovery by spontaneous imbibition of modified salinity brine in fractured reservoirs.