An Innovative Approach To Integrate Fracture, Well Test and Production Data into Reservoir Models
- Asnul Bahar (Kelkar & Assocs. Inc.) | Harun Ates (Kelkar & Assocs. Inc.) | Maged H. Al-Deeb (Abu Dhabi Co. for Onshore Oil Operation) | Salem El Abd Salem (ADCO) | Hussein S. Badaam (Abu Dhabi Co. for Onshore Oil Operation) | Steef J. Linthorst (Shell U.K. Ltd.) | Mohan G. Kelkar (U. of Tulsa)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2005
- Document Type
- Journal Paper
- 325 - 336
- 2005. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 5.6.4 Drillstem/Well Testing, 5.6.1 Open hole/cased hole log analysis, 5.8.6 Naturally Fractured Reservoir, 3.3.6 Integrated Modeling, 1.6.9 Coring, Fishing, 5.5.8 History Matching, 5.5 Reservoir Simulation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.8.7 Carbonate Reservoir, 4.1.5 Processing Equipment, 3.3.2 Borehole Imaging and Wellbore Seismic, 1.6 Drilling Operations, 5.1.5 Geologic Modeling, 3.3.1 Production Logging, 4.1.2 Separation and Treating, 5.5.7 Streamline Simulation, 5.1 Reservoir Characterisation
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This paper presents an innovative approach to integrate fracture, well-test,and production data into the static description of a reservoir model as aninput to the flow simulation. The approach has been implemented successfully ina field study of a giant naturally fractured carbonate reservoir in the MiddleEast. This study was part of a full-field integrated reservoir-characterizationand flow-simulation project.
The main input available for this work includes matrix properties andfracture-network, well-test, and production data. Stochastic models of matrixproperties were generated using a geostatistical methodology based on welllogs, core, seismic data, and geological interpretation. The fracture networkwas described in the reservoir as lineaments (fracture swarms) showing twomajor fracture trends. The network and its properties (i.e., fractureporosities and permeabilities) were generated by reconciling seismic, well-log,and dynamic data (Well Test and Production Log Tool, PLT).
The challenge of the study is to integrate all the input in an efficient andpractical way to produce a consistent model between static and dynamic data. Asa result, it is expected to reduce the history-matching effort. This challengewas solved by an innovative iterative procedure between the static and dynamicmodels.
The static part consists of the calibration of model permeability to matchthe well-test permeability. It is done by comparing their flow potentials, kh.In this analysis, the dominant factor in controlling production at each well,either matrix or fracture, was determined. Based on the dominant factor, matrixor fracture permeability was modified accordingly. This way, the changes inpermeability are consistent with the geological understanding of the field.
The dynamic part was carried out through a full-field flow simulation tointegrate production data. The flow simulation at this stage was used to matchproduction capacity, [i.e., to determine whether the given permeability (matrixand fracture) distribution is enough to produce the fluid at the specifiedpressure during the producing period of the well]. The iteration is stoppedonce a reasonable production-capacity match is obtained. In general, a goodmatch was achieved within three to four iterations. The generated reservoirdescription is expected to substantially reduce the effort required to obtain agood history match.
This paper presents the approach, implementation, and results of afracture-integration process into a reservoir model. The study is part of afully integrated reservoir-characterization and flow-simulation study of an oilfield in the Middle East. A comprehensive integrated reservoir characterizationwas conducted by considering all available data, namely well logs and cores,geological interpretation, seismic (structures and inversion-derived porosity),fracture network, and pressure-buildup (PBU) tests. The approach used in thestudy was a stochastic approach in which multiple reservoir descriptions weregenerated to quantify the uncertainty in future performance.
Reservoir properties for each realization were generated with ageostatistical technique that produces properties (i.e., porosity,permeability, and water saturation) consistent with the underlying rock-typedescription. The description was based on core and log data. Additionally,porosity, which affects the permeability description, was also constrained tothe seismic-derived porosity. The permeability distribution generated by thismethod is referred to as the core-derived permeability in this paper. Becausecore measurement commonly represents the matrix property of the rock, thecore-derived permeability mentioned above was also referred to as matrixpermeability.
It is commonly observed that the well-test permeability values do not matchthe thickness-weighted core-permeability averages. This is partly because ofthe differences in the measurement scales of core samples, which cover a fewinches, and well tests, which investigate several hundred feet around thewellbore. In addition, the presence of fractures and/or high-permeabilitychannels will further enhance the difference between the two sources of data.The mismatch between these two permeabilities may be small or as high as threeorders of magnitude. Therefore, reservoir descriptions based on coremeasurements alone cannot honor the well-test results and need to be modifiedproperly.
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