Journal of Petroleum Exploration and Production Technology最新文献

The thermal recovery is facing the challenge of improving quality and efficiency. Steam-assisted gravity drainage (SAGD) steam circulation has a profound implications for the formal production. There is a lack of research on the situation when the steam injection pressure of injection well is lower than that of production well during startup stage. In this paper, the effects of diverse injection pressure difference on SAGD steam circulation and production stage are investigated by analytical modeling and numerical simulation, especially when the steam injection pressure of producer well is larger than that of injection well, and reliable results are obtained by temperature falloff test and model comparison validation. Meanwhile, in order to minimize the factors affecting the simulation accuracy, a sensitivity analysis of the temperature prediction model for the startup stage is carried out using Monte Carlo method and the finest possible mesh is used in the numerical simulation. The results show that:①The preheating results are faster and more uniform than the conventional preheating method when steam injection pressure of producer is greater than that of injector, and the subsequent production indexes are also superior to those of the conventional preheating method. ②The injected steam temperature had the greatest effect on the prediction accuracy of the analytical model; The finer the numerical simulation grid division, the lower the midpoint temperature of the horizontal well pair; ③An optimal range of injection pressure differences that achieves the best balance between preheating efficiency and thermal recovery effectiveness is achieved with P_prod-P_inj in the range of 400–500 kPa. ④The preheating method investigated in this paper minimizes the effect by unfavorable factors such as reservoir non-homogeneity, which holds the potential for more uniform, time-saving preheating and without the addition of field equipment.

In heavy oil reservoirs, favorable reservoir properties have a positive impact on the production performance during CO₂ huff-n-puff. It is significant to study the screening method of applicable conditions for CO₂ huff-n-puff in actual reservoirs. To solve these problems, this paper introduced the orthogonal design method to analyze the main factors based on numerical simulation. The technical analysis and the economic evaluation were both employed to obtain the applicable conditions of selecting oil layers or injection wells during CO₂ huff-n-puff. And a new algorithms of machine learning, the random forest algorithm, was introduce to find the weighted factors and the scoring standards that were suitable for CO₂ huff-n-puff. Finally, a set of method for screening suitable reservoir conditions was established. Based on the introduction of orthogonal analysis method and random forest algorithm, a software was established to achieve the purpose of analyzing the feasibility of CO₂ huff-n-puff considering different reservoir geological parameters. This method increased the accuracy and efficiency in screening reservoir conditions that was suitable for CO₂ huff-n-puff.

Detection of the low resistivity-low contrast (LRLC) reservoirs is among the main challenges in the oil industry. In this concern, the LRLC pay zones of the Upper Messinian Abu Madi clastic reservoirs in the onshore Nile Delta Gas fields became a main challenge for significant exploration. This type of reservoirs, including low resistivity-low contrast zones and thin-bedded intervals, are often overlooked using the conventional petrophysical evaluation techniques, especially in the wildcat exploratory wells or highly agitated shoreline depositional environments like the Nile Delta of Egypt. These hidden low contrast reservoirs are generally challenging due to the presence of many shale intercalations/laminations and/or due to increasing the shale volume represented in the form of dispersed distribution, and the dominance of conductive clay minerals. Therefore, in this study, the expected high resistivity values of the gas-bearing reservoir intervals of the Abu Madi Formation in the onshore Begonia gas Field, as a typical case study of the LRLC reservoirs, are masked due to the relatively high shale conductivity, particularly when the thickness of these intervals is less than the vertical resolution of the utilized conventional resistivity log. To verify the LRLC phenomena of the Begonia gas Field, the obtained data was compared to the South Abu El Naga gas Field as a normal case study with a relatively high resistivity gas-bearing pay zone. To overcome the impact of the conductive clay mineral content and identify these hidden low resistivity reservoir intervals, it is necessary to integrate the conventional logging data (gamma-ray, shallow and deep resistivity, density, and neutron) with the acoustic log data including shear and compressional sonic data. In this way, a useful relationship can be established enabling the detection of these hidden LRLC reservoir intervals. This integration is based on the principle that shear waves are not influenced by the fluids types, whereas the compressional sonic waves are influenced by the reservoir fluids. However, to effectively investigate these concealed LRLC reservoir intervals, which can boost production and increase the potential reserves, it is essential to have a low water cut value. The present study represents introduces an efficient workflow, which can be extended to other similar LRLC pay zones in the Nile Delta and northeast Africa. It is also extendible to the LRLC reservoirs in similar deltaic systems having conductive minerals-bearing reservoirs or thin beds.

Stylolites possess a dual function in assessing the quality of the Lower Cretaceous carbonate reservoir in the Abadan Plain, Zagros Basin. They can either operate as barriers or facilitate the flow of fluids. To investigate this, we conducted a comprehensive study using core-plug samples, thin section petrography, high-resolution computed tomography, geochemical analysis, and petrophysical evaluation. Our findings indicate that stylolite surfaces can enhance effective porosity and connectivity by acting as open pathways. In the Fahliyan Formation, stylolites can be classified into four types based on their characteristics, including shape, size, amplitude, and the presence of insoluble material in the seams. The genetic type of stylolites is determined by the dominant stress direction, while various parameters in the burial diagenetic stage, such as pressure, temperature, and the presence of soluble ion-rich fluids, can affect porosity and permeability. Stylolites in the Fahliyan facies create continuous and connected porosity for fluid flow, with their amplitude and morphology impacting reservoir quality, especially in mud-supported facies. Therefore, the presence of stylolites in mud-supported facies can improve porosity and permeability. Dissolution, reduced overburden pressure, and horizontal compression are the main factors that expose the stylolite surfaces in the Fahliyan Formation. The extent of cementation, which is the primary barrier feature, varies significantly across the Fahliyan Reservoir in the Abadan Plain Zone due to the degree of stylolitization in the examined facies. However, our findings from wells and geological data combination indicate that reservoir quality in the examined formation facies is significantly influenced by various conditions, with a particular emphasis on the type of fluid flow in the passages.

This work presents a numerical study incorporating the impact of temperature variations along the fracture on the viscosity of fracturing fluids and consequently on proppant distribution in hydraulic fracturing. Traditional models have not considered non-uniform temperature distributions, resulting in less accurate predictions of proppant migration and distribution. The proposed model integrates the thermal variations to enhance the understanding of proppant dynamics under realistic field conditions. The proposed model is validated through physical experiments, demonstrating significant differences in proppant placement due to temperature- induced viscosity changes. Our results show that proppant distribution is substantially affected by lower temperatures at the fracture opening and higher temperatures at the distal end, contrasting sharply with distribution patterns observed under uniform viscosity conditions. As the temperature at the fracture opening decreases, the viscosity of the fracturing fluid increases, enhancing its capacity to transport proppant. The increased viscosity facilitates the transport of proppant deeper into the fracture, resulting in a reduction of the total amount of proppant near the fracture opening and a smaller stacking angle compared to those observed at fixed viscosities of 10, 100, and 200 mPa sThe findings offer critical insights into the mechanics of proppant flow, holding substantial theoretical and practical implications for optimizing hydraulic fracturing treatments.

The coal-rock reservoir exhibits a dual porous medium characteristic, where fractures are the primary contributor to permeability, while pore structure influences the gas adsorption properties of coal rock. Gas adsorption induces swelling in the coal matrix, leading to a reduction in fracture width and subsequently causing decreased permeability and reduced well production. Investigating the impact of geological characteristics of coal-rock on gas adsorption and desorption properties can enhance our understanding of the patterns governing changes in coal-layer production. This study focused on the 3^# coal seam in China's Qinshui Basin as its research subject. It involved an analysis of mineral composition, physical properties, gas content, and pore structure characteristics to explore the adsorption traits of different gases and conduct experimental studies on variations in gas adsorption and desorption capabilities under diverse conditions. The research findings suggest that the coal rock in the study area is primarily characterized by micropores and small pores, with well-developed larger pores and fractures. The pore connectivity is somewhat limited, and the predominant pore size ranges from 100 to 200 nm. The average permeability measures 0.198 × 10^–3 µm², while the mean specific gas content stands at 21.7 m³/t. Analysis of the isothermal adsorption curve reveals a substantial increase in adsorption when pressure falls below 3.5 MPa due to a steep slope; as pressure continues to rise, there is a gradual upward trend in adsorption until reaching 8 MPa, after which point adsorption increases slowly and stabilizes. Results from binary gas adsorption–desorption experiments indicate low desorption levels and rates for CO₂ components compared to relatively higher desorption amounts and rates for CH₄ components. Furthermore, it was observed that CO₂ has a displacement effect on CH₄; higher CO₂ concentrations are more conducive to CH₄ release and CO₂ storage.

Nanopores are in dominant positions in tight reservoirs. Recently, global scholars have focused on the role of imbibition in tight reservoirs on the macroscale, which is insufficient for understanding the process and mechanism of imbibition in tight reservoirs on the microscale. Therefore, it is of great significance to adopt a new microscopic research method to study the imbibition of water in micropore and nanopore spaces in tight reservoirs. In this paper, models of the quartz nanopore imbibition effect and water drive oil reservoirs are established through molecular dynamics simulation. Then, the impacts of different factors on the imbibition effect and the roles of this effect in the water drive process are investigated. The results show that the percolation rate of water in the nanopore is related to the temperature, pore size, and wettability. The permeation strength increases with increasing wettability. Warming accelerates the movement of water molecules in the system, thereby increasing the rate of osmosis, enhancing the strength of osmosis, and shortening the time needed for equilibrium. However, the total amount of osmosis remains unchanged. The smaller the pore size is, the stronger the sorption strength. Imbibition plays a dominant role at lower injection rates, and expulsion plays a dominant role as the injection rate gradually increases.

Low salinity water alternating immiscible gas CO₂ (Immiscible CO₂-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO₂ flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO₂-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO₂-LSWAG injection outperforms conventional immiscible CO₂ and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of Catboost and LightGBM used in this study produced R² scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of CatBoost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications.

This study employs comprehensive source rock evaluation using seismic inversion, rock-eval pyrolysis, organic petrography and basin modeling techniques. The kerogen type is determined by using the Van Krevelen diagram, confirming Bahu-01, Nandpur-01 and Zakria-01 wells have kerogen type III, whereas Panjpir-01 well exhibits kerogen type II to III. The TOC was calculated using core data from (34) samples by employing organic geochemistry technique and post stack seismic inversion, applied on 2D seismic data. Bahu-01 well indicates poor source rock potential, with an average TOC value of 0.34%. In contrast, Panjpir-01 and Nandpur-01 wells represent moderate to good organic richness, with average TOC values of 1.25% and 1.36%, respectively. However, Zakria-01 well with a TOC of 0.72%, exhibits fair organic richness. The Maturity estimation from organic petrography and basin modeling reveals that the Bahu-01, Panjpir-01, and Nandpur-01 wells have average vitrinite reflectance (%Ro) values below 0.50, indicating immature source rock. In contrast, the average %Ro value for the Zakria-01 well is 0.63, confirming the source maturity in the early oil window, with peak generation occurring during Eocene age. Finally, the source rock evaluation proves the source is mature in the western part only and future hydrocarbon exploration should be focused in the western area. The integrated source rock evaluation approach is novel in Punjab Platform. The diverse methodologies enhanced our understanding about source rock characteristics for pursuing hydrocarbon resources. An integrated approach will also provide valuable insights for hydrocarbon exploration in numerous other basins worldwide.

Sand control is an ongoing challenge in numerous hydrocarbon-producing wells in sand-rich reservoirs. Sand production in these wells can cause damage to equipment, reduce production rates, and lead to erosion that can damage subsea equipment, production equipment, well completions, and surface facilities. This problem can compromise the mechanical integrity of the well, resulting in reduced hydrocarbon production and increased operating expenses. This review evaluates various sand production mechanisms, including geological and mechanical production methodologies, and fluid-related aspects, which are thoroughly investigated to offer a thorough understanding of the complexity of the issue and the state of sand prediction approaches. Empirical correlations, numerical simulations, and analytical models are among the sand production prediction techniques critically assessed in this study. The benefits, drawbacks, and suitability of these techniques for various reservoir environments are discussed. Furthermore, the potential benefits of combining Fiber optic (FO) technologies and machine learning (ML) for real-time monitoring and mitigation are highlighted. This integrated strategy has the potential to transform sand control practices of the industry, as demonstrated by case studies and new research that highlights its effectiveness. The future vision outlined in this review includes developments in automation, data processing methods, and sensor technologies, which should improve the precision and dependability of sand production predictions and mitigation. In conclusion, this review paper provides an extensive analysis of the current level of prediction techniques, as well as the mechanisms behind sand production in oil and gas wells. This highlights how real-time, data-driven solutions for monitoring and addressing sand production problems may be provided by FO and ML, which can ultimately lead to safer and more effective hydrocarbon recovery operations.