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Practical Methods to Improve Storage Operations - A Case Study (SPE 57460)


An Appalachian case study by Brown and Sawyer demonstrates that multi-rate well testing can be used to distinguish mechanical skin from rate-dependent (non-Darcy) effects.  This paper also cites a GRI study in which 60% of the reservoirs evaluated had wells in which non-Darcy flow was a significant damage mechanism.


A case study is presented in which a storage operator in the northeast US used easily obtainable data to: (1) accurately quantify both existing and potential deliverability of the reservoir; (2) identify bottlenecks in downhole and surface facilities that reduce maximum deliverability; (3) resolve inventory issues by determining the effective reservoir pore volume; (4) demonstrate the feasibility of turning the working gas volume multiple times per season; and (5) determine the optimum horsepower requirements to turn the working gas volume multiple times per season. All of this was accomplished by analysis of inexpensive and easily collected data.

The general optimization process used is outlined and discussed, as well as the limitations of the approach. The value of this optimization process is demonstrated by presenting field case histories that review the results of the optimization process and the implications to the storage operator.

Optimization Process

Sometimes the optimization of gas storage operations requires very rigorous methodology, such as full-field simulation [1,2]. Full-field simulation using a numerical finite-difference model can be used to optimize damage remediation efforts, locate new wells, and evaluate various operational scenarios. In this paper, we discuss an alternative approach to finite-difference modeling to optimize gas storage reservoirs.

The storage field optimization process used in the case studies discussed below can be divided into five general tasks:

  1. Characterization of the individual well/completion.
  2. Characterization of the reservoir.
  3. Construction of a total system model.
  4. Cataloging of system inefficiencies (“bottlenecks”).
  5. Construction of predictive tools.

This process can be used to optimize an existing storage reservoir, but should not be used when new wells are a consideration.

Author(s): SPE, SPE, K.G. Brown, W.K. Sawyer, Holditch - Reservoir Technologies

Paper Number: SPE 57460

URL: https://www.onepetro.org/conference-paper/SPE-57460-MS


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