Fractured reservoirs are distributed widely over the world, and describing fluid flow in fractures is an important and challenging topic in research. Discrete fracture modeling (DFM) and equivalent continuum modeling are two principal methods used to model fluid flow through fractured rocks. In this paper, a novel method, embedded discrete fracture modeling (EDFM), is developed to compute equivalent permeability in fractured reservoirs. This paper begins with an introduction on EDFM. Then, the paper describes an upscaling procedure to calculate equivalent permeability. Following this, the paper carries out a series of simulations to compare the computation cost between DFM and EDFM. In addition, the method is verified by embedded discrete fracture modeling and fine grid methods, and grid-block and multiphase flow are studied to prove the feasibility of the method. Finally, the upscaling procedure is applied to a three-dimensional case in order to study performance for a gas injection problem. This study is the first to use embedded discrete fracture modeling to compute equivalent permeability for fractured reservoirs. This paper also provides a detailed comparison and discussion on embedded discrete fracture modeling and discrete fracture modeling in the context of equivalent permeability computation with a single-phase model. Most importantly, this study addresses whether this novel method can be used in multiphase flow in a reservoir with fractures.

To obtain a reliable production forecast, one has to establish a geological model with well logs and seismic data. The geological model usually has to be upscaled using certain upscaling techniques. Then, a dynamic reservoir model is constructed with another dataset, including completion data, production data, fluid properties, and relative permeability curves. At last, the dynamic model needs to be validated by a history matching process. This approach is data-intensive, time-consuming, and often not rigorously accomplished due to the lack of skillset and time. In this study, 10,000 groups of reservoir/completion input data were generated by Latin hypercube sampling method, and then, 10,000 groups of output (oil rate and cumulative production data) were obtained by numerical simulation. Next, a machine learning technique was applied to establish a model between the input data and determining parameters of a decline curve analysis model by fitting the generated cumulative production rate. Overall coefficients of determination ( R 2 ) of the three Arps decline curve factors were 0.966, 0.990, and 0.945. The validation result shows that the production rate and cumulative production predicted by the proposed machine learning–decline curve analysis (ML-DCA) model agreed well with those simulated by reservoir simulation. As a result of the ML-DCA regression model, a complete understanding can be established of the impact of reservoir properties on the DCA model. The proposed ML-DCA model not only provides a quick and robust method for petroleum engineers to estimate production performance for unconventional reservoirs from reservoir and completion properties without full-field geocellular modeling but also can be used to optimize the completion and operation parameters for wells of interest.

Over the past decades, CO2 flooding in low-permeability porous media has continued to grow. Furthermore, propelled by the current movement toward CO2 sequestration, the flooding of CO2 is expected to further increase. Thus, it is of scientific significance to study the mechanism of displacement of CO2 flooding as well as the CO2 storage characteristics. In this study, CO2 miscible/immiscible flooding experiments were conducted on two low permeability samples to study the difference between them. Specifically, the effect of oil displacement and CO2 storage coefficient for different displacement models were measured and compared. In addition, nuclear magnetic resonance (NMR) tests and frozen pelleting experiments were conducted. The NMR tests clarified the lower limit of pore throat where oil can be displaced by CO2 miscible/immiscible flooding. Furthermore, the frozen pelleting experiments analyzed the distribution characteristics of the remaining oil before and after CO2 miscible/immiscible flooding. The investigation showed that the development efficiency of CO2 miscible flooding is higher than CO2 immiscible flooding in low permeability reservoirs. The results also indicated that CO2 miscible flooding increases the recovery of hard-to-produce semi-bound oil phase and CO2 storage coefficient with CO2 miscible displacement is more significant than that with CO2 immiscible flooding. The data from this study is a critical input for reservoir simulation, which sheds light on the CO2 miscible/immiscible flooding in low permeability reservoirs.

A local refined model, using micro-seismic data to model fracture geometry, is presented to study huff-n-puff surfactant injection in a tight oil reservoir. The goal of this study is to understand the key parameters that control the surfactant huff-n-puff performance in tight oil reservoirs. In this new approach, natural fractures in tight oil reservoir is described by dual permeability model, and stimulated reservoir volume (SRV) based on micro-seismic datais is modeled by local refined grid. In the study, sensitivity analysis is carried out to optimize oil recovery, such as wettability change, interfacial tension, surfactant adsorption, huff-n-puff cycle, etc. The results indicate that surfactant injection is a favorable method to mobilize oil in tight oil reservoirs; wettability alteration and interfacial tension of surfactant are the dominant mechanisms for the oil recovery through surfactant injection; surfactant adsorption is a key element to the success of the wettability alteration process; and soaking time does not have obvious impact on recovery. The incremental oil recovery factor over primary production for 15 years of total production is up to 3.5% of OOIP that doubles the recovery from the primary production. The study gives new method to study surfactant injection in the tight oil reservoirs when micro-seismic data available. It can be helpful for modeling other EOR process in tight oil reservoirs. The results also can guide surfactant injection in field development for similar tight oil field.