Performance Drivers in Restimulation of Gas-Storage Wells
- Shahab Mohaghegh (West Virginia U.) | Khalid Mohamad (West Virginia U.) | Popa Andrei (West Virginia U.) | Ameri Sam (West Virginia U.) | Dan Wood (CNG Transmission)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2001
- Document Type
- Journal Paper
- 536 - 542
- 2001. Society of Petroleum Engineers
- 4.6 Natural Gas, 6.1.5 Human Resources, Competence and Training, 2.5.1 Fracture design and containment, 7.6.6 Artificial Intelligence, 2.5.2 Fracturing Materials (Fluids, Proppant), 4.1.2 Separation and Treating, 5.1.2 Faults and Fracture Characterisation, 1.6 Drilling Operations, 4.1.5 Processing Equipment, 5.6.1 Open hole/cased hole log analysis, 2.4.3 Sand/Solids Control, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation
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To maintain or enhance deliverability of gas-storage wells in the Clinton sand in northeast Ohio, an annual restimulation program was established in the late 1960s. The program calls for as many as 20 hydraulic fractures and refractures per year. Several wells have been refractured three to four times, while there are still wells that have only been fractured once in the past 30 years.
As the program continues, many wells will be stimulated a second, third, or fourth time. This paper summarizes an attempt to carefully study the response of the Clinton sand to hydraulic fractures and to identify the performance drivers in each series of fracture jobs. Are the performance drivers the same for the later fractures (second, third, and fourth fracture jobs) as they were for the first ones, or do they change? This paper attempts to answer such questions.
Identification of major performance drivers becomes important when new jobs are to be designed. They not only play an important role in enhancing the response of the wells to new stimulation jobs, but they also may prove to be an important economic factor in the design of new stimulation procedures. If, for instance, it is concluded that an increase in proppant volume does not influence the stimulation outcome after the second refracture, then fewer resources can be used for proppant volume and can be directed toward parameters that are more influential.
This study employs a combined neural-network and fuzzy-logic tool to identify the performance drivers.
In many industrial and manufacturing processes, it is important to know the role and influence of each component or parameter in the process outcome. Oilfield operations are no exception. Such information can contribute significantly to process efficiency and prevent wasteful usage of the resources. If the engineer knows in advance which component is the main driver of the process performance, efforts can be concentrated on manipulating that component to achieve the desired outcome. On the other hand, a lack of such information can result in wasting resources by using greater amounts of a component that does not make a significant difference in the process outcome and, therefore, increasing the cost and reducing the efficiency of the process.
The conventional method of approaching this problem is to build an accurate mathematical model of the process and perform parametric sensitivity analysis on the model. The authors believe that developing a mathematical model is the best approach and that, whenever possible, all efforts should be concentrated on developing such models and performing detailed model analysis.
The reality of many complex processes, including some in the petroleum and natural gas industry, is that there are no known mathematical models that can accurately describe these processes. The problem being discussed in this paper is one such example. Restimulation of gas-storage wells is a convoluted and complex problem that cannot be modeled mathematically for many reasons. The most important reasons for the inability to construct a mathematical model for restimulation of gas-storage wells are the lack of detailed reservoir data and the complexity of modeling a stimulated reservoir (and its response to restimulation). As has been shown in the past,1-3 the next best thing to mathematical or numerical modeling of a complex problem is the use of virtual intelligence techniques (neural networks, fuzzy logic, and genetic algorithms) to approximate the process behavior.
In analyzing petroleum and natural gas engineering problems, when a mathematical model of a complex process cannot be constructed, other means have been used to identify the most influential parameters. These approaches can be as simplistic as statistical methods like linear regression analysis or as complex as fuzzy curves4 and neural networks.5 In this paper, we apply all these methods to the problem of restimulation of gas-storage wells and discuss their potential shortcomings. We also discuss two important issues and provide some ideas that might shed some light on the problem at hand. The first issue concerns the use of fuzzy curves in identifying the most important parameters and provides an extension that might help to further solidify their contribution to problems such as the one discussed here. The second issue is related to the use of neural networks to identify important parameters in a complex problem. In this paper, we demonstrate the usefulness and value of backward elimination neural-network analysis in providing important information about the process being analyzed.
Statement of the Problem
The restimulation of gas-storage wells discussed here takes place in the Clinton sand in northeast Ohio. A hydraulic fracturing and refracturing program has been in place in this field for approximately three decades. There are storage wells in this field that have not been hydraulically fractured, as well as storage wells that have been refractured more than four times in the past 25 years. Every year, several wells are selected for stimulation to maintain deliverability of the field. Identification of the most influential parameters in this process is an important part of designing new fractures and refractures to maximize return on investment.
The data set used in this study was constructed with the well files. The following parameters were extracted from the well files for each hydraulic fracture treatment: the year the well was drilled, the total number of fractures performed on the well, the number of years since the last fracture, fracture fluid, amount of fluid, amount of sand used as proppant, sand concentration, acid volume, nitrogen volume, average pumping rate, and the name of the service company performing the job. The goal of the study is to identify the most important and most influential of the above-mentioned parameters when they are correlated with post-fracture deliverability. The matchup between hydraulic fracture design parameters and the available post-fracture deliverability data produces a data set with approximately 560 records. Post-fracture deliverability in this study refers to the actual peak deliverability after the well is cleaned up and before the new decline in production begins.
Using straightforward regression analysis that can be performed with any widely available spreadsheet software, the data set shows that some trends may be identified quickly on these fracture jobs. Table 1 shows the ranking of the correlation between each parameter and the post-fracture deliverability. The ranking is based on the calculated R2 values.
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