Optimal Infill Well Locations in a Mature Oil Field Using Manual and Search-Based Optimization Methods With Reservoir Flow Simulation

2014 ◽  
Author(s):  
Wathiq Jassim Mohammed Al-Mudhafer
Keyword(s):  
Flow Simulation ◽  
Optimization Methods ◽  
Oil Field ◽  
Reservoir Flow

Reservoir Flow Simulation study for a Sector in Main Pay-South Rumaila Oil Field

2010 ◽  
Author(s):  
Wathiq Jassim Mohammed ◽  
Mohammed S. Al Jawad ◽  
Dunia Abdulshaib Abdu Al-Shamaa
Keyword(s):  
Simulation Study ◽  
Flow Simulation ◽  
Oil Field ◽  
Reservoir Flow ◽  
South Rumaila Oil Field

Multiscale Integration for Karst-Reservoir Flow-Simulation Models

2020 ◽  
Vol 23 (02) ◽  
pp. 518-533
Author(s):  
Manuel Gomes Correia ◽  
João Carlos von Hohendorff Filho ◽  
Denis José Schiozer
Keyword(s):  
Flow Simulation ◽  
Simulation Models ◽  
Reservoir Flow ◽  
Karst Reservoir

Coupled Reservoir Flow Simulation: Fault Leakage Analysis

2009 ◽  
Author(s):  
Luis Glauber Rodrigues ◽  
Luciane Bonet Cunha ◽  
Richard J. Chalaturnyk
Keyword(s):  
Flow Simulation ◽  
Reservoir Flow ◽  
Fault Leakage

Data Assimilation of Coupled Fluid Flow and Geomechanics Using the Ensemble Kalman Filter

SPE Journal ◽  
2010 ◽  
Vol 15 (02) ◽  
pp. 382-394 ◽  
Author(s):  
Haibin Chang ◽  
Yan Chen ◽  
Dongxiao Zhang
Keyword(s):  
Fluid Flow ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Material Properties ◽  
Ensemble Kalman Filter ◽  
Oil Field ◽  
Reservoir Rock ◽  
Model Parameters ◽  
Stress Strain ◽  
Reservoir Flow

Summary In reservoir history matching or data assimilation, dynamic data, such as production rates and pressures, are used to constrain reservoir models and to update model parameters. As such, even if under certain conceptualization the model parameters do not vary with time, the estimate of such parameters may change with the available observations and, thus, with time. In reality, the production process may lead to changes in both the flow and geomechanics fields, which are dynamically coupled. For example, the variations in the stress/strain field lead to changes in porosity and permeability of the reservoir and, hence, in the flow field. In weak formations, such as the Lost Hills oil field, fluid extraction may cause a large compaction to the reservoir rock and a significant subsidence at the land surface, resulting in huge economic losses and detrimental environmental consequences. The strong nonlinear coupling between reservoir flow and geomechanics poses a challenge to constructing a reliable model for predicting oil recovery in such reservoirs. On the other hand, the subsidence and other geomechanics observations can provide additional insight into the nature of the reservoir rock and help constrain the reservoir model if used wisely. In this study, the ensemble-Kalman-filter (EnKF) approach is used to estimate reservoir flow and material properties by jointly assimilating dynamic flow and geomechanics observations. The resulting model can be used for managing and optimizing production operations and for mitigating the land subsidence. The use of surface displacement observations improves the match to both production and displacement data. Localization is used to facilitate the assimilation of a large amount of data and to mitigate the effect of spurious correlations resulting from small ensembles. Because the stress, strain, and displacement fields are updated together with the material properties in the EnKF, the issue of consistency at the analysis step of the EnKF is investigated. A 3D problem with reservoir fluid-flow and mechanical parameters close to those of the Lost Hills oil field is used to test the applicability.

Staggered In Time Coupling of Reservoir Flow Simulation and Geomechanical Deformation: Step 1 - One-Way Coupling

1999 ◽  
Author(s):  
Susan E. Minkoff ◽  
Charles M. Stone ◽  
J. Guadalupe Arguello ◽  
Steve Bryant ◽  
Joe Eaton ◽  
...  
Keyword(s):  
Flow Simulation ◽  
Reservoir Flow ◽  
Deformation Step ◽  
Geomechanical Deformation

Tie Simplex Based Parameterization for Compositional Reservoir Simulation

2008 ◽  
Author(s):  
Denis Voskov ◽  
Hamdi A. Tchelepi
Keyword(s):  
Method Of Characteristics ◽  
Flow Simulation ◽  
Deep Understanding ◽  
General Purpose ◽  
Two Phase ◽  
Compositional Flow ◽  
Reservoir Flow ◽  
Tie Lines ◽  
Solution Route ◽  
Tie Line

In this work, we generalize the Compositional Space Parameterization (CSP) approach, which was originally developed for compositional two-phase reservoir flow simulation. Tie-line based parameterization methods [1]–[3] were motivated by insights obtained from MOC (Method of Characteristics) theory. The MOC based analytical theory [4] has provided deep understanding of the interactions between thermodynamics and flow. In our adaptive framework, tie-lines are used to represent the solution route of multi-component multiphase displacements. The tie-line information is used as a preconditioner for EOS computations in general-purpose compositional flow simulation.

Application of Reservoir Flow Simulation Integrated with Geomechanics in Unconventional Tight Play

2017 ◽  
Vol 51 (1) ◽  
pp. 315-328 ◽  
Author(s):  
Menglu Lin ◽  
Shengnan Chen ◽  
Ernest Mbia ◽  
Zhangxing Chen
Keyword(s):  
Flow Simulation ◽  
Reservoir Flow
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