Numerical Modeling of Coupled Fluid Flow and Geomechanical Stresses in a Petroleum Reservoir
01 Jun 2020-Journal of Energy Resources Technology-transactions of The Asme (American Society of Mechanical Engineers Digital Collection)-Vol. 142, Iss: 6
TL;DR: In this article, a fully coupled hydro and geomechanical model has been used to predict the transient pressure disturbance, reservoir deformation, and effective stress distribution in both homogeneous and heterogeneous reservoirs.
Abstract:
A fully coupled hydro and geomechanical model has been used to predict the transient pressure disturbance, reservoir deformation, and effective stress distribution in both homogeneous and heterogeneous reservoirs. The heterogeneous reservoir is conceptualized by explicitly considering the spatial distributions of porosity and permeability as against assuming it as constant values. The finite element method was used in the coupled model in conjunction with the poroelasticity. Transient pressure disturbance is significantly influenced by the overburden during the production in both homogeneous and heterogeneous reservoirs for all the perforation schemes. Perforation scheme 2 provides the optimum reservoir performance when compared with other three schemes in terms of transient pressure distribution and reservoir subsidence. It also has the ability to overcome both the water and gas coning problems when the reservoir fluid flow is driven by both gas cap and water drive mechanisms. A Biot–Willis coefficient is found to significantly influence both the pressure and stress distribution right from the wellbore to the reservoir boundary. Maximum effective stresses have been generated in the vicinity of the wellbore in the reservoir at a high Biot–Willis coefficient of 0.9. Thus, the present work clearly projects that a Biot–Willis coefficient of 0 cannot be treated to be a homogeneous reservoir by default, while the coupled effect of hydro and geomechanical stresses plays a very critical role. Therefore, the implementation of the coupled hydro and geomechanical numerical models can improve the prediction of transient reservoir behavior efficiently for the simple and complex geological systems effectively.
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TL;DR: In this article, an integrated machine learning (ML)-response surface model (RSM)-autoregressive integrated moving average (ARIMA) model was used to enhance the heat production from a geothermal reservoir.
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Abstract:
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11 citations
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Abstract:
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5 citations
TL;DR: In this article , the probabilistic analysis of land subsidence due to pumping is performed by Biot's poroelasticity and random field theory based on a case study.
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3 citations
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TL;DR: In this article, the authors show that hydrogen in an equiatomic CoCrFeMnNi high-entropy alloy leads not to catastrophic weakening, but instead increases both, its strength and ductility.
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TL;DR: In this paper, the authors present an approach for predicting the performance of conventional and unconventional gas reservoirs. But their approach is limited to two stages: phase 1.1 Phase 1.2 Phase 2.3 Phase 3.4 Phase 4.5 Tracys Form of the MBE 5.1.
Abstract: 1. Well Testing Analysis 1.1 Primary Reservoir Characteristics 1.2 Fluid Flow Equations 1.3 Transient Well Testing 1.4 Type Curves 1.5 Pressure Derivative Method 1.6 Interference and Pulse Tests 1.7 Injection Well Testing 2. Water Influx 2.1 Classification of Aquifers 2.2 Recognition of Natural Water Influx 2.3 Water Influx Models 3. Unconventional Gas Reservoirs 3.1 Vertical Gas Well Performance 3.2 Horizontal Gas Well Performance 3.3 Material Balance Equation for Conventional and Unconventional Gas Reservoirs 3.4 Coalbed Methane CBM 3.5 Tight Gas Reservoirs 3.6 Gas Hydrates 3.7 Shallow Gas Reservoirs 4. Performance of Oil Reservoirs 4.1 Primary Recovery Mechanisms 4.2 The Material Balance Equation 4.3 Generalized MBE 4.4 The Material Balance as an Equation of a Straight Line 4.5 Tracys Form of the MBE 5. Predicting Oil Reservoir Performance 5.1 Phase 1. Reservoir Performance Prediction Methods 5.2 Phase 2. Oil Well Performance 5.3 Phase 3. Relating Reservoir Performance to Time 6. Introduction to Oil Field Economics 6.1 Fundamentals of Economic Equivalence and Evaluation Methods 6.2 Reserves Definitions and Classifications 6.3 Accounting Principles References Index
215 citations
TL;DR: This Article contains an error in Figure 4A, where the LV-VP16-CREB-GFP + NAN-190 representative image is incorrect and the Figure legend is correct.
Abstract: Scientific Reports 6: Article number: 29551; published online: 12 July 2016; updated: 24 February 2017 This Article contains an error in Figure 4A, where the LV-VP16-CREB-GFP + NAN-190 representative image is incorrect. The Figure legend is correct. The correct Figure 4A appears below as Figure 1.
155 citations
TL;DR: In this paper, the porosity correction depends on the pore compressibility factor and a mechanical contribution that can be expressed either in terms of volumetric strain, pore volume change, or the mean total stress change.
Abstract: During high porosity reservoir production, the rock compaction is a complex phenomenon that depends on the rock constitutive behavior, the reservoir stress path, etc. Reservoir compaction can hardly be analyzed with conventional reservoir simulators as the pore compressibility factor, the only mechanical parameter used in such simulators, is not sufficient to represent the complex phenomena involved. In order to solve correctly this problem, the full thermohydro- mechanical equations must be addressed. The corresponding set of equations can be either solved simultaneously (fully coupled scheme) or using a conventional reservoir simulator in conjunction with a geomechanical simulator and information exchanges between the two simulators (partial coupling). The paper presents three formulations of the partial coupling, which are obtained in the framework of single-phase flow and a linear elastic isotropic rock behavior. This simple framework makes possible an easy and rigorous derivation of the porosity correction to be appended to the reservoir Lagrange's porosity used in the reservoir simulator. The porosity correction depends on the pore compressibility factor and a mechanical contribution that can be expressed either in terms of volumetric strain, pore volume change, or the mean total stress change. One formulation is tested on a numerical test that depicts the water flood through a laboratory core sample initially saturated with oil and constrained to uniaxial strain. The numerical test illustrates the importance of the mechanical effects on the fluid flow problem and validates the partial coupling proposed. The example also highlights the role of the pore compressibility factor in the partially coupled reservoir simulation. Actually, in the partially (iteratively) coupled approach, the pore compressibility factor can be interpreted as a relaxation parameter controlling the convergence speed of the iterative process between reservoir simulation and geomechanical simulation.
97 citations