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Collaborating Authors
Modeling & Simulation
Characterization of Multiphase Flow in Shale Oil Reservoirs Considering Multiscale Porous Media by High-Resolution Numerical Simulation
Lei, Zhengdong (Research Institute of Petroleum Exploration and Development, PetroChina (Corresponding author)) | Li, Junchao (School of Mechanical Engineering, Xi'an Shiyou University) | Chen, Zhewei (Research Institute of Petroleum Exploration and Development, PetroChina) | Dai, Xu (Exploration and Development Institute, Daqing Oilfield Co., Ltd) | Ji, Dongqi (Research Institute of Petroleum Exploration and Development, PetroChina) | Wang, Yuhan (School of Energy Resources, China University of Geosciences) | Liu, Yishan (Research Institute of Petroleum Exploration and Development, PetroChina)
Summary Multiphase flow behavior in the complex porous media of lacustrine shale oil is critical to the oil production performance. Shale oil reservoir processes spatial spaces of multiscale porous media. In view of the mesoscopic scale, the fluids flow behaviors of shale oil reservoirs are significantly different from that of tight oil reservoirs and the multimedia flow fluxes in shale have to be reevaluated. Furthermore, upscaling methods from the mesoscopic scale to the macroscopic level as well as coupling methods of multimedia mass transfer have not been adequately established. Based on the multiple media model, such as organic-inorganic matter system and embedded discrete fracture model (DFM), this work proposes a multiscale porous media flow simulation method that is applicable for lacustrine shale oil reservoirs. In the model, various reservoir matrix and flow spaces, such as organic pores, intragranular pores, intergranular pores, and fracture networks composed of bedding fractures and hydraulic fractures, are included. Methods of mass flux conductivity estimations among multiscale media and the relevant upgrading methods are also proposed. Validation of the model is first conducted by the comparison of the oil production performance estimated by the proposed model and a theory solution, and the model is further compared to Gulong shale oil reservoir data to determine its availability in field application. The results show that the proposed simulation model is capable of accurately characterizing the multiphase flow characteristics in multiscale media in shale. It is further demonstrated that the proposed model significantly improves the simulation accuracy over the current nonupscaling models. Field study shows that, based on the accurate characterization of the complex flows in shale oil reservoirs, the research output can provide support for future development of the Gulong shale oil reservoir.
- North America > United States (1.00)
- Asia > China > Heilongjiang Province (0.46)
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (1.00)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Yian Formation (0.99)
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Mingshui Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
Abstract It is challenging to reliably identify fluid components and estimate their saturations in formations with complex lithology, complex pore structure, or varying wettability conditions. Common practices for assessing fluid saturations rely on the interpretation of resistivity measurements. These techniques require model calibration, which is time consuming/expensive and can only differentiate conductive and nonconductive fluids. Interpretation of 2D NMR maps provides a viable alternative for identifying fluid components and fluid volumes. However, conventional techniques for the interpretation of 2D NMR rely on cutoffs in the T1-T2 or D-T2 maps. The application of cutoffs is prone to inaccuracies when fluid-component relaxation responses overlap. To address these shortcomings, we introduce a new workflow for identifying/tracking fluid components and estimating their volumes from the interpretation of 2D NMR measurements. We developed a workflow that approximates 2D NMR maps with a superposition of 2D Gaussian distributions. The algorithm automatically determines the optimum number of Gaussian distributions and their corresponding properties (i.e., amplitudes, variances, and means). Next, a clustering technique is implemented to the dataspace containing the Gaussian distribution parameters obtained for the entire logged interval. Each Gaussian is assigned to a cluster corresponding to different pore/fluid components. We then calculate the volumes under the Gaussian distributions corresponding to each cluster at each depth. The volumes associated with each cluster translate directly into the pore volumes corresponding to the different fluid components (e.g., heavy/light hydrocarbon, bound/free water) at each depth. A highlighted contribution of this work is that, in contrast to the alternative petrophysical interpretation techniques for fluid characterization, the introduced workflow does not require calibration efforts, user-defined cutoffs, or proprietary data sets. Furthermore, approximating 2D NMR data with a superposition of Gaussian distributions improves the accuracy of estimated pore volumes of fluid components with overlapping NMR responses. The clustering using the Gaussian distribution parameters as inputs enables depth tracking of different fluid components without making use of user-defined 2D cutoffs. Finally, the multidimensional nature of the introduced clustering provides the unique capability of identifying different fluid components with 2D NMR response located in the same range of coordinates in a T1-T2 map. We successfully verified the reliability and robustness of the new workflow for enhancing petrophysical interpretation in two organic-rich mudrock formations with complex mineralogy and pore structure.
- North America > United States > Texas (1.00)
- Europe (0.93)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.67)
- Geology > Geological Subdiscipline > Mineralogy (0.61)
- South America > Argentina > Patagonia > Neuquén > Neuquen Basin > Vaca Muerta Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- (25 more...)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.69)
Abstract This paper describes a proposed high-specification standard format that is ideally suited for the data management of definitive records of wellbore logs. For this reason, it is a good standard for data exchange between applications. The format is suitable for complex three-dimensional (3D) data, including those generated by deep azimuthal resistivity (DAR) and ultradeep azimuthal resistivity (UDAR) tools, acoustic borehole reflection images, vertical seismic profiles (VSP), borehole imaging tools, multifingered caliper logs, and array data with multiple depths of investigation. It is applicable for use with logging-while-drilling (LWD) and wireline-conveyed logging tools. The format also naturally collapses down when utilized to store simple conventional logs that contain one value per depth in the wellbore. The proposed format provides spatial details of every data point collected by or interpreted from a wellbore-logging tool. The position of each data point is defined by reference back to the measure point of the sonde, which in turn is defined by the wellbore deviation survey and its coordinate reference system (CRS). Each data point in space may have an unrestricted number of parameters. An example might be most likely horizontal and vertical resistivity, maximum value based on uncertainty, minimum value based on uncertainty, and flags indicating the data position with respect to depth of detection (DOD). The new proposed format is so versatile. It is suitable as an Open Group Open Subsurface Data Universe (OSDU) standard to store and exchange all data measured by logging tools in a wellbore and can possibly be extended to include all well data (for example, core, cuttings, and more). The proposed format requires a detailed definition so that computer scientists can implement it in applications used for subsurface modeling. The OSDU will also require this detailed definition in order to adopt it as a standard.
- Europe (1.00)
- North America > United States (0.92)
- Information Technology > Information Management (0.35)
- Information Technology > Modeling & Simulation (0.34)
- Information Technology > Data Science > Data Integration (0.34)
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.34)
Reservoir modeling is the process of creating a three-dimensional representation of a given reservoir based on its petrophysical, geological and geophysical properties. These properties are defined during reservoir characterization where geoscientists and engineers gather all physical and chemical data to extrapolate those values throughout the reservoir. They can then create a three-dimensional model to be used for reservoir simulation. During reservoir simulation, scientists run several computer models of the reservoir, with real time field data, to accurately predict the behavior of the reservoir. This is useful for making field development decisions, such as drilling additional wells and estimating reserves.
- Information Technology > Modeling & Simulation (0.77)
- Information Technology > Communications > Collaboration (0.40)
The multi-disciplinary integration of geosciences has made a lot of progress in the last decade; but this progress is often limited to the early stages of the production chain (e.g., acquisition-processing) and mostly to two techniques (e.g., seismic-gravity, active-passive seismic). Rarely does the integration include three or more data types and seldom can we quantify the achieved benefits in terms of production increase. The latter fact is due to the limited involvement of petroleum engineering and reservoir simulation in the preliminary data acquisition, processing and interpretation. This workshop aimed to define the state-of-the-art in this integration process, highlighting current weaknesses and discussing what needed to be developed further. Papers included topics of reservoir simulation of different scenarios based on different data availability as a validation tool and feedback for further tuning.
- Information Technology > Modeling & Simulation (0.85)
- Information Technology > Communications > Collaboration (0.40)
Achieving effective and efficient Integrated Reservoir Modeling (IRM) workflows and processes is a key goal for oil and gas companies. However, despite the fact that IRM process is a well-defined objective, it continues to be very challenging and hard to achieve primarily due to its non-uniqueness. The IRM process involves multi-disciplinary data gathering and integration, appropriate software/workflows to enable domain-experts communication and collaboration, adequate project timing and cost allocation, and most importantly, a consistent and systematic industry-wide strategy. This workshop focused on the IRM's best practices used for building multi-disciplinary static and dynamic reservoir simulation models for both conventional and unconventional reservoirs. In the IRM process, a wide range of subsurface data types are involved, in addition to the associated uncertainties, workflows and ideas that feed into it.
- Information Technology > Modeling & Simulation (0.74)
- Information Technology > Communications > Collaboration (0.52)
This filter is called the prediction-error filter or the prediction-error operator. There are α 1 {\displaystyle \alpha -1} zeros in the prediction-error operator that lie between the leading coefficient, namely 1, and the negative prediction-operator coefficients. The Z-transform of the zero-delay unit spike is 1.
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Communications > Collaboration (0.40)
- Information Technology > Modeling & Simulation (0.76)
- Information Technology > Communications > Collaboration (0.40)
Dr. Richard Groshong is Professor Emeritus of the Department Geological Sciences at the University of Alabama. He received his BS at Bucknell University in 1965, his MA at the Unversity of Texas at Austin in 1967 and his Ph.D. at Brown University in 1971, all in geology. Professor Groshong's research program focuses on the interpretation, validation, and prediction techniques for compressional, extensional, and vertical structures. He and his students are exploring the predictive capabilities of 2-D and 3-D structural balancing, restoring, and modeling techniques. The primary objects of study are the extensional and compressional structures of the Black Warrior foreland basin and the adjacent Appalachian Valley and Ridge province, the extensional and vertical structures of the northern Gulf of Mexico basin, and laboratory models.
- North America > United States > Texas (0.30)
- North America > United States > Alabama (0.30)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.30)
- Geology > Sedimentary Basin > Foreland Basin (0.30)
- Information Technology > Modeling & Simulation (0.66)
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
In general, simulation is a theoretical or a physical representation of an operation and an imitation of its system/processes in real-life. Reservoir Simulation is a field developed in petroleum engineering where it utilizes porous media in computer modeling to estimate the fluids dynamics, its goal is to predict the field performance under varies producing strategies. Reservoir Simulation is grounded on recognized engineering equations, engineers started calculating reservoir engineering with basic mathematical model long before the emergence of modern technology. Although Reservoir simulation is not new to the industry, it has become more efficient than before due to the advanced capabilities provided by modern day technology. Proficiency, efficiency and effectiveness are the reasons why many engineers became a competent to the model and its development.
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Communications > Collaboration (0.50)