Static and Dynamic Uncertainty Management for Probabilistic Production Forecast in Chuchupa Field, Colombia
- Nestor Rivera (Chevron Corp.) | Nestor Saul Meza (Chevron Corp.) | Jeoung Soo Kim (Chevron ETC) | Peter Andrew Clark (Chevron Corp.) | Raymond Garber (Chevron Corp.) | Andres Fajardo (Chevron Corp.) | Victoria Pena (Ecopetrol)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2007
- Document Type
- Journal Paper
- 433 - 439
- 2007. Society of Petroleum Engineers
- 1.2.3 Rock properties, 5.5.3 Scaling Methods, 7.2.2 Risk Management Systems, 4.1.4 Gas Processing, 4.1.5 Processing Equipment, 5.5.8 History Matching, 5.6.1 Open hole/cased hole log analysis, 5.1.1 Exploration, Development, Structural Geology, 5.1.5 Geologic Modeling, 5.5 Reservoir Simulation, 5.6.9 Production Forecasting, 1.14 Casing and Cementing, 4.6 Natural Gas, 5.4.2 Gas Injection Methods, 5.1 Reservoir Characterisation, 5.2 Reservoir Fluid Dynamics, 2.4.3 Sand/Solids Control, 5.6.3 Deterministic Methods, 5.3.2 Multiphase Flow, 1.6.9 Coring, Fishing, 5.6.4 Drillstem/Well Testing, 5.4.3 Gas Cycling, 4.2 Pipelines, Flowlines and Risers, 5.1.8 Seismic Modelling
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Structural, stratigraphic, and petrophysical uncertainties result in a wide range of geologic interpretations. For fields with a long production and pressure history, 3D dynamic simulations have been very useful in providing feedback to geologic modelers, which results in improved static models. For this study, we developed an integrated static and dynamic workflow to create a range of probabilistic simulation models to forecast dry-gas production under several production scenarios in the Chuchupa field.
We selected eight geologic interpretations, representing the range of original gas in place (OGIP) and reservoir geometries determined in the static modeling, to perform dynamic history matches. The OGIP range of the models with very good history matches corresponds closely to the P10 to P90 OGIP range calculated from static modeling.
In addition, we calibrated the various models with historical bottomhole and tubinghead flowing pressures and coupled the reservoir model with a network consisting of surface lines and equipment, pipelines from two platforms to the onshore sale-point station, and multistage compression to 1,215 psia. The set of probabilistic models is currently used to evaluate various production and market scenarios.
Chuchupa field has produced 1.9 Tscf of dry gas, or approximately 40% of the OGIP. At the time of this study, three new horizontal wells were being planned, and new gas-sales agreements were being considered. Recent seismic reinterpretation, a new stratigraphic study, and a revision of the petrophysical model resulted in new probabilistic static models for the field.
While these static models were being built, a parallel numerical-simulation study was conducted to determine the range of OGIP values that could be successfully history matched. Nine numerical reservoir models were generated by applying pore-volume multipliers to the prior-generation reservoir model, yielding a range of OGIP from 3.8 to 6.6 Tscf. We attempted to history match each of these nine models by using an optimization routine to adjust aquifer support, vertical transmissibility across a potential seal, and rock compressibility. The optimization routine proved to be a very useful and efficient tool to attain good-quality history matches in short periods of time. Good matches were obtained for models with OGIP ranging from 4.3 to 5.8 Tscf.
On the basis of this information, the geologic modelers revised petrophysical parameters and generated 27 static models, encompassing three structural interpretations, three porosity distributions, and three possible positions of the gas/water contact (GWC). From experimental design, we obtained P10, P50, and P90values of 4.1, 4.7, and 5.3 Tscf, respectively. We scaled up and built reservoir-simulation models on eight of these models and performed history matches. The observed parameters to match were static well pressures and the absence of water production. Six of the eight models were satisfactorily history matched, with reasonable adjustments to aquifer strength, vertical transmissibility, and rock compressibility. The successfully history-matched models are within the P10 to P90 OGIP range.
We selected three models to forecast future gas production. These models match the P10, P50, and P90 OGIP values determined in the probabilistic static model and combine the low, mid, and high structures, porosity and Swi distributions, and the range of GWC positions.
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Archie, G.E. 1942. The Electrical Resistivity Log as anAid in Determining Some Reservoir Characteristics. Trans., AIME146: 54-62. SPE-942054-G.
Carter, R.D. and Tracy, G.W. 1960. An Improved Method for CalculatingWater Influx. Trans., AIME 219: 415-417.SPE-1626-G.
Haldorsen, H.H. and Damsleth, E. 1990. Stochastic Modeling. JPT42 (4): 404-412. SPE-20321-PA. DOI: 10.2118/20321-PA.
Hall, H.N. 1953. Compressibility of ReservoirRocks. Trans., AIME 198: 309-311. SPE-953309-G.
Hunter, V.F. 1979. The Geology of theChuchupa and Ballena Gas Fields of Northwestern Colombia. Report No. 871, TexasPetroleum Co. Geology Dept., Bogotá.
Landa, J.L. and Guyaguler, B. 2003. A Methodology for History Matchingand the Assessment of Uncertainties Associated With Flow Prediction. PaperSPE 84465 presented at the SPE Annual Technical Conference and Exhibition,Denver, 5-8 October. DOI: 10.2118/84465-MS.
Lee, J. and Wattenbarger, R.A. 1996.Gas Reservoir Engineering. Textbook Series, SPE, Richardson, Texas5.
McCarthy, E.L., Boyd, W.L., and Reid,L.S. 1950. The Water Vapor Contentof Essentially Nitrogen-Free Natural Gas Saturated at Various Conditions ofTemperature and Pressure. Trans., AIME 189: 241-242.SPE-950241-G.
Persad, S. 2005. Evaluation of Multiphase-FlowCorrelations for Gas Wells Located Off the Trinidad Southeast Coast. PaperSPE 93544 presented at the SPE Latin American and Caribbean PetroleumEngineering Conference, Rio de Janeiro, 20-23 June. DOI:10.2118/93544-MS.
Pittman, E.D. 1992. Relationship ofPorosity and Permeability to Various Parameters Derived From Mercury InjectionCapillary Pressure Curves for Sandstones. AAPG Bull. 76 (2):191-198.
Reservoirs. 1993. Worldwide RockCatalog School. Robert Sneider Exploration, Houston.
Schlumberger. 2004. SimOpt UserGuide, Houston.
Spivey, J.P., Brown, K.G., Sawyer, W.K.,and Frantz, J.H. 2004. EstimatingNon-Darcy Flow Coefficient From Buildup-Test Data With Wellbore Storage.SPEREE 7 (4): 256-269. SPE-88939-PA. DOI:10.2118/88939-PA.
Yeten, B., Castellini, A., Guyaguler, B.,and Chen, W.H. 2005. A ComparisonStudy on Experimental Design and Response Surface Methodologies. Paper SPE93347 presented at the SPE Reservoir Simulation Symposium, Houston, 31January-2 February. DOI: 10.2118/93347-MS.