The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Yatsenko, V. M. (Rosneft Oil Company) | Gavrilova, E. V. (Rosneft Oil Company) | Toropov, K. V. (Rosneft Oil Company) | Burakov, I. M. (ROSPAN INTERNATIONAL JSC) | Makaev, R. I. (RN-BashNIPIneft LLC) | Latypov, I. D. (RN-BashNIPIneft LLC) | Kolonskikh, A. V. (National Gas Company LLC)
The Bazhenov formation is the largest oil and gas source formation in the West Siberian oil and gas bearing province, whose reserves are classified as hard-to-recover. The Bazhenov formation deposits are both the most significant hydrocarbons source rocks in the province and an independent oil and gas reservoir. Formation has a complex geological structure, which directly affects the mobile hydrocarbon reserves heterogeneous distribution. Analysis of implementing approaches experience to the unconventional reservoir development regarding to Bazhenov formation reservoirs shows that the reservoirs distribution is characterized by lateral variability and is not controlled by a structural factor. The lack of sure signs of reservoir evolution identifying zones and assessing their productivity is one of the most important risks achieving successful Bazhenov reserves development. The sweet spot zones are interpreted to be laterally limited areas in which the source rock maturity degree is sufficient to form liquid and moving hydrocarbons in sufficient quantities to operate wells that will be profitable. This paper presents a method for localizing Bazhenov formation promising zones which is based on the kerogen transformation model with a prediction of the achievable pore pressure, taking into account the material balance and rock stress. The approach is based on a consistent physical and mathematical model that describes the kerogen conversion kinetics with a subsequent increase in pore pressure in the source rock and the formed liquid hydrocarbons vertical migration into the nearest reservoirs due to auto-fluid fracturing of the clay tight formation-barriers for the oil source rock. The simulation result is visual map of prospective areas for the Bazhenov formation development that allows to determine the priority well drilling areas.
Summary The fast marching method (FMM)-based rapid flow simulation has been shown to accelerate simulation efficiency by orders of magnitude by transforming 3D simulation to equivalent 1D simulation using the concept of the “diffusive time of flight” (DTOF). However, the 1D transformation does not directly apply to multiwell problems. In this paper, we propose a novel DTOF-based multidomain multiresolution discretization scheme to accelerate multiwell simulation of unconventional reservoirs. Our method formulates multiwell simulation problems based on the DTOF which displays the pressure front propagation in unconventional reservoirs. The DTOF contours are used to partition the reservoir into local and shared domains. A local domain is where the flow is dominated by a single well, and the shared domain is where the fluid flow is influenced by multiple wells. The DTOF contours expand independently in local domains and interfere in the shared domain. After the partitioning, each domain is discretized using a multiresolution scheme whereby the original 3D fine mesh is preserved near the wells to account for detailed physics including gravity, and the rest of the domain is discretized into 1D mesh based on the DTOF contours to alleviate the simulation workload. The power and efficacy of our approach are demonstrated using synthetic and field-scale simulation models with different degrees of geologic and well-completion complexity. The simulation results, number of active cells, and computation time for the proposed discretization scheme are compared with the original high-fidelity 3D model for each case. The results show that the proposed method is suitable for multiwell simulation problems in unconventional reservoirs and can accelerate flow simulations by orders of magnitude with minimal loss of accuracy. The novelty of this work is the creation of DTOF-derived multiresolution discretization with local and shared domains to simplify and accelerate the calculation of subsurface flow problems, especially in unconventional reservoirs. Our workflow can be easily interfaced with commercial simulators, making it suitable for large-scale field applications.
Summary In-situ combustion (ISC) is a promising thermal enhanced oil recovery method with benefits for deep reservoirs, potentially lesser energy requirements as compared to steam injection, and low opportunity cost. Although successful ISC projects have been developed all over the world, challenges still exist including difficulties in monitoring combustion-front progress in the field, describing multiscale physical processes, characterizing crude oil kinetics fully, and simulating ISC at field scale. This work predicts combustion front propagation and the effect of thermally induced stress at the scale of an ISC pilot project. Reservoir deformation was characterized by a geomechanical model to investigate the correlation of combustion front progress with reservoir and surface deformation. We upscaled the reaction kinetics directly from combustion tube experiments and calibrated the laboratory-scale model compared with experimental measurements. We then upscaled numerical simulation to a 3D geometry incorporating a geomechanical model. The change in scale is significant as the combustion tube is 6.56 ft (2 m) in length, whereas the dimensions of the 3D model are 1,440 ft by 1,440 ft (439 m) by 1,400 ft (427 m). The elastic properties were defined by Young’s modulus and Poisson’s ratio, whereas the plastic properties were defined by a Mohr-Coulomb model. A sensitivity study examined the reliability of the model, showing the reaction progress and geomechanical responses were not significantly impacted by gridblock dimensions and reservoir heterogeneity. Finally, a field-scale model was developed covering an area of 5,960 ft (1817 m) by 4,200 ft (1280 m). We observed successful ISC simulation including ignition as air injection started. The temperature increased immediately to more than 800°C (1,400°F) based on the chemical kinetics implemented. The temperature history indicated that the combustion front propagated from the injection well into the reservoir with an average velocity of 0.16 ft/D (0.049 m/d). A surface deformation map correlated with the progress of ISC in the subsurface. Land surface uplift because of ISC ranges from 0.1 ft (0.03 m) to several feet, depending on the rock properties and subsurface events. This proof-of-concept model indicated strong potential to detect the surface movement using interferometric synthetic aperture radar (InSAR) and/or tiltmeters to monitor dynamically combustion front positions in subsurface.
Any reservoir simulator consists of n m equations for each of N active gridblocks comprising the reservoir. These equations represent conservation of mass of each ofn components in each gridblock over a timestep Δt from tn to tn 1 . The firstn (primary) equations simply express conservation of mass for each of n components such as oil, gas, methane, CO2, and water, denoted by subscript I 1,2,…,n. In the thermal case, one of the "components" is energy and its equation expresses conservation of energy. An additional m (secondary or constraint) equations express constraints such as equal fugacities of each component in all phases where it is present, and the volume balanceSw So Sg Ssolid 1.0, whereS solid represents any immobile phase such as precipitated solid salt or coke. There must be n m variables (unknowns) corresponding to these n m equations. For example, consider the isothermal, three-phase, compositional case with all components present in all three phases.
Hassan, Anas Mohammed (Department of Petroleum Engineering, Khalifa University of Science and Technology, UAE) | Tackie-Otoo, Bennet N. (Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Malaysia) | Ayoub, Mohammed A. (Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Malaysia) | Mohyaldinn, Mysara E. (Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Malaysia) | Al-Shalabi, Emad W. (Department of Petroleum Engineering, Khalifa University of Science and Technology, UAE) | Adel, Imad A. (Department of Petroleum Engineering, Khalifa University of Science and Technology, UAE)
Abstract This contribution is a progressive effort to investigate the effect of the novel hybrid EOR method of Smart Water Assisted Foam (SWAF) technique on oil recovery from carbonates through numerical modeling. In this work, a core-scale model was utilized to provide an insight and a better understanding of the controlling mechanisms behind incremental oil recovery using a new hybrid EOR method consisting of a combination of smart water flooding and foam injection, termed as Smart Water Assisted Foam (SWAF) technology, particularly for carbonate reservoirs. A core-scale model encapsulating the physics of SWAF flooding was used to history-match experimental data and the model was further optimized utilizing the CMG simulator. For extracting the most value from this numerical investigation, a sensitivity analysis was performed to monitor the effect of influential parameters affecting oil recovery depending on the spectrum of the experimental data available. The objective functions used in the sensitivity analysis include minimizing the history-matching global error and maximizing the oil recovery profiles. Three sensitivity analysis approaches were used: Tornado-plot, SOBOL analysis, and MORRIS analysis. For generating the related proxy models, polynomial regression, and radial basis function (RBF) neural networks were investigated. Subsequently, the DECE-based and PSO-based optimization methods were employed to examine the effect of chemical design parameters such as smart water (Mg), surfactant aqueous solution (SAS), and foam concentrations along with the liquid production rate on the oil recovery factor during SWAF-flooding. Based on the numerical results, the experimental coreflooding data were accurately history-matched using the proposed model with a minimal error of 4.74% applying the PSO-based optimization method. Furthermore, in terms of the objective function prediction during the sensitivity analysis study, the comparative assessment of both proxy models on the verification plot reveals that the RBF neural network outperforms the polynomial regression. Consolidated findings from the three sensitivity analyses, i.e., the Tornado-plot, SOBOL, and MORRIS, outline three common parameters that significantly affect the oil recovery profiles that are liquid production rate (LigProdCon), foam (DTRAPW SAS2), and Mg concentration (DTRAP Mg3) parameters. On the other hand, in terms of maximizing the oil recovery while minimizing the usage of injected chemicals during SWAF flooding, the optimal solution via the PSO-based approach is superior (97.89%) to the DECE-based optimal solutions (92.47%). This work presents one of the few studies investigating the numerical modeling of the SWAF process and capturing its effects on oil recovery. The optimized core scale model can be further used as a base for building a field-scale model and designing a successful pilot project.