SPE Reservoir Evaluation & Engineering
Volume 10, Number 4, August 2007, pp. 433-439

SPE-100526-PA

Static and Dynamic Uncertainty Management for Probabilistic Production Forecast in Chuchupa Field, Colombia

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DOI  More information 10.2118/100526-PA http://dx.doi.org/10.2118/100526-PA

Citation

  • Rivera, N., Meza, N., Kim, J., Clark, P., Garber, R., Fajardo, A. and Peña, V. 2007. Static and Dynamic Uncertainty Management for Probabilistic Production Forecast in Chuchupa Field, Colombia.  SPE Res Eval & Eng  10 (4): 433-439. SPE-100526-PA.

Discipline Categories

  • 6.5 Reservoir Simulation
  • 6.5.2 Construction of Static Models
  • 6.5.5 Evaluation of Uncertainties

Summary

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.

Introduction

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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History

  • Original manuscript received: 5 March 2006
  • Meeting paper published: 15 May 2006
  • Revised manuscript received: 19 January 2007
  • Manuscript approved: 19 January 2007
  • Version of record: 20 August 2007