Using Reservoir Modeling To Evaluate Stimulation Effectiveness in Multilayered "Tight" Gas Reservoirs: A Case History in the Pinedale Anticline Area
- Stephen K. Schubarth (Schubarth Inc) | John Paul Spivey (Phoenix Reservoir Engineering) | Paul Thomas Huckabee (Shell E&P Co.)
- Document ID
- Society of Petroleum Engineers
- SPE Gas Technology Symposium, 15-17 May, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2006. Society of Petroleum Engineers
- 5.2 Reservoir Fluid Dynamics, 2.4.3 Sand/Solids Control, 5.1.5 Geologic Modeling, 4.1.2 Separation and Treating, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.1 Reservoir Characterisation, 3.3.1 Production Logging, 5.6.4 Drillstem/Well Testing, 4.1.5 Processing Equipment, 5.1.1 Exploration, Development, Structural Geology, 5.5 Reservoir Simulation, 5.5.8 History Matching, 2.5.2 Fracturing Materials (Fluids, Proppant)
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Low permeability, or "tight??, gas reservoirs are being developed at an ever increasing rate in the U.S. The amazing increase in activity in the Rocky Mountain region over the past decade is a testament to this. Currently, there are several "tight?? gas plays in the U.S. that involve the commingling of multiple intervals in order to gain economic viability. The Pinedale Anticline of southwestern Wyoming is one of these areas. The Pinedale Anticline completions pose a particularly complex problem when attempting to evaluate the "best?? method of stimulation because as many as twenty-two separate stimulation treatments are placed in up to 70 discrete sand intervals over a gross interval up to 6,000 feet thick. Evaluations are further complicated by variation in permeability exceeding two orders of magnitude and pore pressures increasing from 0.44 psi/ft to 0.83 psi/ft.
The analysis of "tight?? gas reservoirs has been the topic of many SPE papers over the past twenty years. Several have presented data indicating the broadness of the permeability distribution which may be encountered when developing these reservoirs.[1,2,3] The broadness of the permeability distribution, often over two orders of magnitude in breadth, poses a statistical problem when trying to simply compare production response of one set of data to another in a given field. We will quantify the significance of this and present statistical evaluations documenting the probability of obtaining two similar data sets with respect to permeability when broad distributions exist. We then compare the size of the sample set necessary to quantify stimulation effectiveness using production alone with the sample size required when using reservoir simulation.
The reservoir simulation analysis presented in the paper demonstrates a process for use in multiple layered reservoirs for evaluating stimulation effectiveness. The process requires significantly fewer field tests than if production rates were used alone. Multiple production logs were utilized over several producing months in selected wells and are crucial to the production history match process. A wide variety of proppant products are investigated and compared to expected performance from published specifications. This paper will aid engineers working in multi-layered reservoirs understand the complexity of the evaluation process and give them a process for evaluating stimulation effectiveness in their reservoirs.
Development of the Pinedale Anticline of southwestern Wyoming has continued at an aggressive pace over the past several years. Massive hydraulic fracturing (MHF) treatments are the only means of stimulating production to economically accecptable levels from the "tight?? gas sands present in this area. Each well being completed generally requires between 14 and 22 MHF treatments in order to effectively produce from all potential pay intervals. Over two million pounds of proppant are often used per well, representing a potential high investment cost to the operator. The ability to evaluate the incremental production benefit associated with the use of one proppant versus another can have a significant impact on the profitability of the field development.
Several studies have been performed in the past which have utilized a comparison of production values to compare the performance of proppants.[1,2,4,7] Other studies have incorporated the use of reservoir simulation to remove reservoir properties variability from the equation.[3,8] Still others have used a normalization process for the removal of reservoir variability.[5,6] Each of these approaches to quantifying the ability of a proppant to increase stimulation effectiveness has its own pros and cons. In the next few paragraphs we will discuss these advantages and disadvantages to each approach and present the premise that our study is based upon.
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