Optimizing Deliverability in Five Gas-Storage Reservoirs—Case Studies

Abstract

Proper identification of damage mechanisms can improve stimulation techniques used in remediation of damaged gas-storage wells. Identifying damage mechanisms is only a beginning step in optimizing remediation. Determining the highest potential candidate wells and evaluating reservoir quality in the field can be just as crucial in optimizing a deliverability enhancement program. Historical data and reservoir/geological description analysis are required to properly rank candidates and design specific treatments to optimize deliverability potential. Once the diagnostic data and analysis are completed, an operator must begin the tedious process of applying the relevant data analysis to rank and validate the candidate wells' potential and select one or more tailored treatment designs. Adequate well ranking is critical to ensure that AFE dollars achieve maximum deliverability. This paper illustrates case studies using a "Solution Team" a multidisciplined team process, in which over 75 wells were diagnosed and treated successfully. Rigorous damage-identification techniques and reservoir quality diagnostics were used in the five gas-storage reservoirs. Each case study produced damage-specific stimulation treatments based on the operator's objectives to enhance existing deliverability. Follow-up evaluations were made at 1- and 2-year intervals to show how the team process that uses new, improved diagnostic practices can optimize deliverability. In this study, damage mechanisms were identified with improved methods described in a previous Gas Research Institute (GRI) project. Damage in each well was quantified using well-test analysis and historical injection/withdrawal cycle performance matching. Log analysis, petrophysical data, geological data, wellbore imaging, and workover historical data were also gathered as treatment-design criteria. The deliverability improvement was quantified for each well using post-treatment diagnostics. The post-treatment evaluations were updated with 1- and 2-year follow-up evaluations. Each study incorporates several unique treatment options addressing a variety of damage mechanisms. Treatments were selected to produce the highest deliverability enhancement and maximize the operator's return on investment. Case Study 1 incorporates high-pressure jetting, tailored acidizing, and hydraulic fracturing techniques used in a deep high-permeability pressure-drive carbonate reservoir. Case Study 2 includes high-pressure jetting and damage-specific fluid treatments in two shallow water-drive clastic reservoirs. Case Study 3 incorporates hydraulic fracturing and high-pressure jetting of a shallow high-permeability pressure-drive clastic reservoir. Case Study 4 incorporates high-pressure jetting with foamed chemical treatments in a converted oil-carbonate reservoir. Introduction This paper demonstrates how an effective diagnostic and ranking process can be used to tailor a well treatment that can optimize deliverability enhancement. In an alliance project with Natural Gas Pipeline (NGPL), now Kinder Morgan, Inc., the objective was to increase deliverability in each of the five existing storage fields described in the case studies.

Proving It - A Review of 80 Published Field Studies Demonstrating the Importance of Increased Fracture Conductivity

“…41 Guoynes et al documented that increased fracture conductivity resulted in deliverability improvements of 109% or 42.9 MMcfd in the North Lansing carbonate reservoir in east Texas. In the Cook's Mills field in central Illinois, wells re-stimulated with higher conductivity fractures achieved 70 to 80% production increases.…”

Effect of Reservoir Properties on the Performance and Design of Gas Storage Wells

“…Optimization of gas storage field development and maximizing production rates has been investigated by several researchers. [1][2][3][4][5][6][7] With the increased demand for natural gas as a clean energy source, many operators investigate different means of increasing performance of their field. [8][9][10] Many design variables such as number of wells, line pressure, wellbore diameter, peak day requirements, and gathering system must be taken into account in the optimization process.…”