Use of Production and Well Test Data with Predictive History Matching to Improve Reservoir Characterization for CO2 Flooding at the South Cowden Unit

1996 ◽  
Author(s):  
K.J. Harpole ◽  
M.G. Gerard ◽  
S.C. Snow ◽  
C.D. Caldwell
Keyword(s):  
Test Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Co2 Flooding ◽  
Well Test ◽  
The South

Understanding subsurface fluvial architecture from a combination of geological well test models and well test data

2018 ◽  
Vol 488 (1) ◽  
pp. 237-257 ◽  
Author(s):  
Patrick William Michael Corbett ◽  
Gleyden Lucila Benítez Duarte
Keyword(s):  
Test Data ◽  
History Matching ◽  
Reservoir Characterization ◽  
Real Life ◽  
Well Testing ◽  
Well Test ◽  
Test Models ◽  
Geological Models ◽  
Single Well ◽  
Porosity And Permeability

AbstractTwo decades of geological modelling have resulted in the ability to study single-well geological models at a sufficiently high resolution to generate synthetic well test responses from numerical simulations in realistic geological models covering a range of fluvial styles. These 3D subsurface models are useful in aiding our understanding and mapping of the geological variation (as quantified by porosity and permeability contrasts) in the near-wellbore region. The building and analysis of these models enables many workflow steps, from matching well test data to improving history-matching. Well testing also has a key potential role in reservoir characterization for an improved understanding of the near-wellbore subsurface architecture in fluvial systems. Developing an understanding of well test responses from simple through increasingly more complex geological scenarios leads to a realistic, real-life challenge: a well test in a small fluvial reservoir. The geological well testing approach explained here, through a recent fluvial case study in South America, is considered to be useful in improving our understanding of reservoir performance. This approach should lead to more geologically and petrophysically consistent models, and to geologically assisted models that are both more correct and quicker to match to history, and thus, ultimately, to more useful reservoir models. It also allows the testing of a more complex geological model through the well test response.

Assimilating Microseismic and Well-Test Data by Use of EnKF for Accurate Reservoir Characterization

SPE Journal ◽  
2014 ◽  
Vol 20 (01) ◽  
pp. 186-201 ◽  
Author(s):  
Mei Han ◽  
Gaoming Li ◽  
Jingyi Chen
Keyword(s):  
Test Data ◽  
Reservoir Characterization ◽  
Weighted Average ◽  
Well Test ◽  
Sampling Errors ◽  
Reservoir Properties ◽  
Reservoir Prediction ◽  
Pressure Transient ◽  
Microseismic Data ◽  
Transient Data

Summary The pressure-transient well-test data can be used to determine the thickness-weighted average permeability in a multilayer reservoir. Injection- or production-profile logs (layer rates), if available, may be used to further quantify the layer properties. This paper explores the possibility of the use of microseismic data in place of injection-/production-profile logs for layered-reservoir characterization. The microseismic first-arrival times from the perforation-timing shots of the test well to monitor wells can only resolve the average velocity along its wavepath but are more sensitive to the layer (or region) with high wave velocity (low productivity). On the contrary, the pressure-transient data are more sensitive to the properties of the high-productivity (high-permeability) layers. Therefore, these two types of data are complementary in reservoir characterization. In this paper, we assimilate these two types of data by use of the state-of-the-art ensemble-Kalman-filter (EnKF) method. Layered-homogeneous- and layered-heterogeneous-reservoir examples verified the complementary nature of these two types of data. The porosities and permeabilities in the layered reservoir obtained after assimilating both types of data are comparable with assimilating pressure-transient and layer-rate data. EnKF is a stochastic process, and the final results may depend on the initial ensemble because of sampling errors, sample size, and nonlinearity of the problem. In this paper, we generated 10 different ensembles for each example for better uncertainty quantification. The paper shows that assimilating pressure-transient data only will yield biased estimates of layered-reservoir properties, whereas assimilating both pressure and microseismic data improves the reservoir-property estimation and reservoir-prediction capabilities.

Calibrate Flow Simulation Models with Well-Test Data to Improve History Matching

1999 ◽  
Author(s):  
Nanqun He ◽  
K.T. Chambers
Keyword(s):  
Test Data ◽  
History Matching ◽  
Flow Simulation ◽  
Simulation Models ◽  
Well Test

Calibration of fluid models using multiphase well test data for improved history matching

2010 ◽  
Vol 75 (1-2) ◽  
pp. 168-177 ◽  
Author(s):  
Shiyi Zheng ◽  
Wenbin Xu
Keyword(s):  
Test Data ◽  
History Matching ◽  
Fluid Models ◽  
Well Test

Reservoir Characterization Constrained to Well Test Data: A Field Example

1996 ◽  
Author(s):  
J.L. Landa ◽  
M.M. Kamal ◽  
C.D. Jenkins ◽  
R.N. Horne
Keyword(s):  
Test Data ◽  
Reservoir Characterization ◽  
Well Test

Depositional Environment Drive as Overpressure Generation: Study Case in “Gap” Field, North West Java Basin

2020 ◽  
Author(s):  
A., C. Prasetyo
Keyword(s):  
Clay Mineral ◽  
Depositional Environment ◽  
Mineral Content ◽  
Hydrocarbon Generation ◽  
Well Test ◽  
Burial History ◽  
The South ◽  
North West ◽  
The North ◽  
Geological Processes

Overpressure existence represents a geological hazard; therefore, an accurate pore pressure prediction is critical for well planning and drilling procedures, etc. Overpressure is a geological phenomenon usually generated by two mechanisms, loading (disequilibrium compaction) and unloading mechanisms (diagenesis and hydrocarbon generation) and they are all geological processes. This research was conducted based on analytical and descriptive methods integrated with well data including wireline log, laboratory test and well test data. This research was conducted based on quantitative estimate of pore pressures using the Eaton Method. The stages are determining shale intervals with GR logs, calculating vertical stress/overburden stress values, determining normal compaction trends, making cross plots of sonic logs against density logs, calculating geothermal gradients, analyzing hydrocarbon maturity, and calculating sedimentation rates with burial history. The research conducted an analysis method on the distribution of clay mineral composition to determine depositional environment and its relationship to overpressure. The wells include GAP-01, GAP-02, GAP-03, and GAP-04 which has an overpressure zone range at depth 8501-10988 ft. The pressure value within the 4 wells has a range between 4358-7451 Psi. Overpressure mechanism in the GAP field is caused by non-loading mechanism (clay mineral diagenesis and hydrocarbon maturation). Overpressure distribution is controlled by its stratigraphy. Therefore, it is possible overpressure is spread quite broadly, especially in the low morphology of the “GAP” Field. This relates to the delta depositional environment with thick shale. Based on clay minerals distribution, the northern part (GAP 02 & 03) has more clay mineral content compared to the south and this can be interpreted increasingly towards sea (low energy regime) and facies turned into pro-delta. Overpressure might be found shallower in the north than the south due to higher clay mineral content present to the north.

Multiple Realizations of the Permeability Field From Well Test Data

SPE Journal ◽  
1996 ◽  
Vol 1 (02) ◽  
pp. 145-154 ◽  
Author(s):  
Dean S. Oliver
Keyword(s):  
Test Data ◽  
Well Test ◽  
Permeability Field
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