Surveillance Modeling And Operational Controls Ensure Integrity of Alaska's Grind And Inject Operations
- K. Zaki (Advantek International Corp, Houston) | Z. Zhai (Advantek International Corp, Houston) | A. Abou-Sayed (Advantek International Corp, Houston) | S. Marinello (Advantek International Corp, Houston) | M. Bill (ASRC Energy Services) | H. Engel (BP)
- Document ID
- American Rock Mechanics Association
- 44th U.S. Rock Mechanics Symposium and 5th U.S.-Canada Rock Mechanics Symposium, 27-30 June, Salt Lake City, Utah
- Publication Date
- Document Type
- Conference Paper
- 2010. American Rock Mechanics Association
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Alaska's Grind and Inject (GNI) operations represent the longest and largest semi-continuous solids waste slurry injection project worldwide. More significant in the current climate of corporate responsibility is that modeling updates and assurance processes allow procedural updates to maintain efficiency and environmental integrity. The modeling program provides data for injection performance analysis and history matching that leads to better understanding of subsurface dynamics. Operational success is evidenced by unblemished capacity to accept large waste volumes with significant ultimate well disposal potential. This paper addresses injection assurance and waste containment throughout project life. The periodic history match of created subsurface features is a major component of this process. Fracture simulation was carried out to match the subsurface response to slurry batch injection through 8 years of injection. The geomechanical modeling necessary to provide the framework on which the simulation works is described. Stress evolution and thermal effects during batch injection is also illustrated for the GNI environments. The disposal domain development has been inferred from the simulation. The provision of designs, solutions and predictions based on the simulation sensitivity studies is described and the impact of field activities is highlighted. Designs, solutions and predictions are given based on the numerical results and sensitivity study verified by past field observations. The verification and updating of the model developed for the GNI operation is provided by the history matching of wellhead pressures through eight years of injection.
The following paper summarizes the background and application of hydraulic fracture modeling to predict the subsurface response to injecting drilling mud and cuttings at the grind and inject (GNI) site. The hydraulic fracturing process has been employed by the oil and gas industry for more than 50 years. Initially, the process was used to enhance near-wellbore permeability of low flow-rate wells, but has since expanded to facilitate sand control, waste disposal and waterflood operations. Beyond the general theory and application, this paper also summarizes the application of hydraulic fracturing at the GNI drilling waste disposal operation. This paper provides background and basis to support continuation of GNI dedicated drilling waste injection program. The information presented in this paper regarding drill cuttings injection (DCI) modeling, technical aspects, and risk management have been derived from a number of sources that include past experience in Alaska's north slope GNI operations, the GRI mounds experiment final reports, open publications, field-acquired data, summary documents and industry open forums.
2. HYDRAULIC FRACTURE MODELING FOR ALASKA GNI PROJECT
This section discusses the fracture modeling for the GNI project. The thermal effects and stress increases related to injection are taken into account in the modeling. In the model, continued injection in existing wells provides the most extreme fracture growth estimate. The modeling process is divided into the following sequence: (i) Perform log analysis to evaluate formation mechanical properties (stresses, Poisson's ratio, Young's modulus and fracture toughness), flow parameters (leakoff coefficient, permeability, porosity, saturation and pore pressure) and thermal properties (linear thermal expansion coefficient).
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