Abstract

About one-fifth of the natural gas used by Americans each winter comes from natural gas storage sites. Gas storage is the primary means for the gas industry to manage fluctuations in supply and demand. Natural gas can be stored in a variety of ways. Most commonly, it is held in underground formations, in depleted oil or gas reservoirs, or in natural aquifers.

Many gas storage wells show a decline in deliverability as a function of time due to several damage mechanisms. The remedial operations such as stimulation and workovers are used to restore the loss in deliverability and to enhance the productivity of a well.

Candidate selection for the stimulation or workover process is generally based on well history. Skin factor is an important parameter to predict the well performance. Skin is usually calculated from a multi rate well test (MRT). However performing a MRT on a regular basis is an unattractive activity when considering the economic issues. First of all, performing a well test may cause temporary production or injection interruptions. Secondly, the cost associated with well test is considered as an operating expense, a fact that does not help the overall economics of operating a Gas Storage Field. Single Rate tests (SRT) are also performed to estimate the deliverability, but they do not contain sufficient data in order to estimate true skin factor.

The objective of this study is to introduce a new methodology to enhance the current practices of estimating true skin factor from a SRT. This method includes history matching of the actual MRT and then estimation of skin value from SRT using the history matched model. Using this methodology it is shown that change in the skin can be estimated with reasonable accuracy.

Introduction

Traditionally, gas storage wells in the Appalachian basin are evaluated by multi rate test or single rate test data. The results of the single rate test data are extrapolated to estimate the Absolute Open Flow (AOF) value of the well. The new value is compared in order to make decision on need for remedial treatments.

Candidate selection is generally based on the well performance history. Skin factor is an important parameter to predict this performance. Change in Skin (?S) generally is a good indicator whether a well needs remedial operations or not. Skin factor is usually calculated from a well test by conventional well test analysis. To maintain an accurate estimate of the well performance for the candidate selection, gas storage wells need to be tested regularly.

However, performing a well test regularly is not quite economic. First, performing a well test may cause temporary production or injection interruptions and second, the cost associated with well test is considered an OpEx (Operating Expense), a fact that does not help the overall economics of operating a Gas Storage field.

Each year Gas Storage operators spend thousands of their OpEx dollars to test storage wells. In industry, multi rate tests (MRT) and single rate tests (SRT) are being performed to have a qualitative assessment for candidate well selection by comparing the AOF (absolute open flow potential) of the wells or they are being performed to have an approximate estimation of the skin factor. Since, a MRT causes temporary production interruptions and a SRT do not contain sufficient data to estimate the "true skin factor", getting the best possible and reliable reservoir and well bore characterization from the performed tests becomes very important.

The objective of this study is to introduce a new methodology to enhance the current practices of estimating the true skin factor from a SRT. This method includes history matching of the actual MRT and then estimation of skin value from SRT using the history matched model. Using this methodology change in skin has been studied.

In this study, optimization of well test analysis has been aimed. This optimization can have two dual benefits for the storage operators. It can reduce OpEx by reducing the number of multi rate tests that must be performed, thus fewer production/ injection interruptions, while by improving the analysis process a more realistic assessment of the damage over time (identified by skin) becomes possible.

Using a commercial numerical simulator and well test data representative of an actual gas storage field located in Ohio we verified the methodology mentioned above.

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