The polymer flooding process has already been applied to the medium permeability type II reservoirs of the Daqing Oilfield (China) to enhance oil recovery. However, this process faces a number of challenges, such as the flooding efficiency, high injection pressure, formation blockage and damage, unbalanced absorption ratio, and economical justification. In this study, single-phase and two-phase flow experiments are performed to investigate polymer injection adaptability with natural cores of type II reservoirs. The enhanced oil recovery (EOR) effects of the polymer are studied by physical simulation experiments, and the results of application in an actual field are also presented. The results indicate that the flow characteristics and injection capability are dominated by the reservoir permeability in polymer flooding. Moreover, the adsorption of polymer molecules and the injection pressure gradient, which reflect formation damage, are affected more significantly by the concentration than by the molecular weight in type II reservoirs. Using the matching relationship, the injection-production process is stable, and additional oil recoveries of 10%–15% can be obtained in heterogeneous type II reservoirs with a high water saturation. This work is significant in that it further accelerates the application of polymer flooding EOR in medium permeability heterogeneous oilfields with high water saturation.

A well test analysis model for fractured and vuggy carbonate reservoir of wells drilling in large scale cave considering wellbore storage and skin factor is established in this paper. The Laplace transformation and Stehfest numerical inversion are applied to obtain the results of wellbore pressure. Through the sensitivity analysis of different parameters for the well test typical curves, it is found that the change of the well test curves is in accordance with the theoretical analysis. With the increase of skin factor, the hump of well test typical curves is steeper. The storage ratio influences the depth and width of the concave in the pressure derivative curves. The cross flow coefficient mainly affects the position of the concave occurrence in the pressure derivative curves. The dimensionless reservoir radius mainly affects the middle and late stages of the log-log pressure type curves, and the later well test curves will be upturned for sealed boundary. The duration of the early stage of the log-log curves will become longer when drilling in large scale cave. The effective well radius is increased to a certain extent, which is in full agreement with the conclusions in this paper. The size of the caves has the same effect on the well test typical curves as wellbore storage coefficient. Due to acidification, fracturing, and other reasons, the boundary of the cave will collapse. Therefore, considering the wellbore storage coefficient and skin effect is very important during well testing. However, the existing models for well testing of fractured and vuggy carbonate reservoir often ignore the wellbore storage coefficient and skin effect. For fractured and vuggy carbonate reservoirs of well drilling in large scale cave, the existing models are not applicable. Since the previous models are mostly based on the triple-porosity medium and the equivalent continuum. The well test model for well drilling in large scale cave of fracture-cavity carbonate reservoirs with wellbore storage coefficient and skin factor in this work has significant application value for oil field.

Natural fractures are common in unconventional reservoirs (e.g., tight oil and gas, shale oil and gas), for which hydraulic fracturing is the main exploitation method. Natural fractures influence the propagation and final transformation effect of hydraulic fractures, which is an important factor for fracture optimization design. In this work, the method of globally embedding zero-thickness cohesive zone elements is applied to establish a dynamic propagation model of hydraulic fractures in naturally fractured reservoirs, and the influence of natural fracture development on the fracture propagation patterns is investigated. The results show that the hydraulic fracture propagation paths in fractured reservoirs are highly complex, and steering, branching, and merging of the propagation paths may occur. When the natural fracture development is small, the fracture propagation pattern is mainly influenced by the minimum horizontal principal stress. In contrast, when the natural fracture development is large, the fracture propagation pattern is influenced by the natural fracture distribution. With increasing natural fracture angle, hydraulic fractures can easily pass through natural fractures and form wide fracturs. Higher numbers of natural fracture groups and fractal dimensions increase the number of fracture propagation directions and communicating natural fractures, and the fractures tend to be narrow and complex.

Decline curve analysis has been used as a reliable method to forecast conventional reservoir well production over the last decades. Recently, an increase in the demand for oil and gas has caused unconventional reservoirs to become a prominent source of energy. However, it is challenged if we still apply the decline curve analysis in unconventional reservoirs due to its limitations such as boundary dominated flow, constant operation condition, et al. Therefore, in this paper, two new methods are proposed using machine learning method to forecast well production in unconventional reservoirs, especially on the EOR pilot projects. The first method is the Neural Network, which allows the analysis of large quantities of data to discover meaningful patterns and relationships. Both peak production rate and hydraulic fracture parameters are used to be the key factors. Lastly, Neural Network technology is applied to investigate the relationship between key factors and oil production rate. The second method uses the Time Series Analysis. Time Series Analysis is one of the most applied data science techniques in business and finance. Since the properties of unconventional reservoir make the production prediction more difficult, it is safe to say that Time Series Analysis can yield good results on the production rate forecast. Field production data from over 1000 wells from different shale plays (Barnett, Bakken, Bone Springs, Eagle Ford oil, Eagle Ford gas, Fayetteville, Marcellus gas, Marcellus oil, Utica oil, and Woodford) is used to verify the feasibility of these two methods. The results indicate there is a good match between the available and predicated production data. The overall R values of Neural Network and Time Series Analysis are above 0.8, which demonstrates that Neural Network and Time Series Analysis are reliable to study the dataset and provide proper production prediction. Meanwhile, when dealing with the EOR production prediction, such as Huff-n-Puff, Time Series Analysis shows more accurate results than Neural Network. This paper proposes a thorough analysis of the feasibility of machine learning in multiple unconventional reservoirs. Instead of repeatedly fitting the production data by decline curve analysis, it also provides a more robust way and meaning reference for the evaluation of the wells.

Numerical well test model of oil-water two-phase flow in fractured and vuggy carbonate reservoir

Hydrogen, a genuinely clean energy, is a promising alternative to fossil fuels. Inspired by underground gas storage of methane, establishing underground hydrogen storage (UHS) in depleted oil and gas reservoirs has emerged as a significant research focus. Carbonate reservoirs, where widely-presented fractures can facilitate the high-speed injection and production of gases, are hence ideal candidates for building underground hydrogen storage facilities. During the cyclic injection and extraction processes of UHS, the formation is subjected to stress disturbances, leading to stress sensitivity. Understanding the stress sensitivity patterns of carbonate rocks is crucial for optimizing injection and production strategies. This study reconstructed three-dimensional digital models of fractured carbonate rocks from the L gas field using micro-CT scanning technology. Utilizing the finite element method, we investigated the microscopic permeability characteristics of carbonate rocks and analyzed the impact of stress loading direction and confining stress on stress sensitivity. The findings reveal that the stress loading direction significantly influences the stress sensitivity of fractured carbonate rocks. When a stress of 60 MPa is applied perpendicular to the fracture direction, the permeability reduction ratio can reach 17.32%. In contrast, when the same stress is applied parallel to the fracture direction, the permeability reduction ratio is only 4.82%. Furthermore, a simulation of UHS with cyclic injection and production of H₂ in the target block was conducted. When both permeability and porosity stress sensitivity were considered, the working gas volume for UHS decreased by only 3.4%, demonstrating that fractured carbonate reservoirs are feasible candidates for constructing underground hydrogen storage.