An Approach to Mitigate Subsidence in Soft Rocks through Coupling Surface Tiltmeter and Injection/Production Data
- Cenk Temizel (Aera Energy) | Harun Kirmaci (Turkish Petroleum) | Turgay Inceisci (Turkish Petroleum) | Zein Wijaya (HESS) | Karthik Balaji (University of Southern California) | Anuj Suhag (University of Southern California) | Rahul Ranjith (University of Southern California) | Minh Tran (University of Southern California) | Basel Al-Otaibi (Kuwait Oil Company) | Ahmad AlKouh (College of Technological Studies) | Ying Zhu (University of Southern California) | Cengiz Yegin (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Heavy Oil Conference and Exhibition, 6-8 December, Kuwait City, Kuwait
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 7 Management and Information, 5.3.4 Integration of geomechanics in models, 5 Reservoir Desciption & Dynamics, 7.2.1 Risk, Uncertainty and Risk Assessment, 7.2 Risk Management and Decision-Making, 7.6.7 Neural Networks, 5.7.2 Recovery Factors, 5.3 Reservoir Fluid Dynamics, 5.7 Reserves Evaluation, 7.6 Information Management and Systems
- surface deformation, neural networks, geomechanics, subsidence, tiltmeters
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Diatomites are high-porosity, low-permeability reservoirs with elastoplastic properties and high geo-mechanical responsiveness. They have a great potential for oil recovery despite these drawbacks. Withdrawal of fluids from the reservoir rock leads to subsidence causing compaction and shear stresses. This disturbed stress distribution results in well failures that causes loss of millions of dollars. Successful maintenance of pressure support through optimum injection/production is key to preventing subsidence to mitigate the risk of well failure and achieve better sweep efficiency for recovery.
There have been different approaches to tackle subsidence and well failures in diatomites, including the use of ‘backpressure method’, coupled with a neural network to optimize injection-production to ‘balance’ the rock in terms of stress-distribution and thus decrease well failure due to shearing. However, using such methods may mask other problems the well is experiencing including several mechanical issues that influence production. Another existing approach, satellite-imaging (InSAR) cannot be used to take real-time actions that is crucial in diatomites.
Surface tiltmeter data is collected to undertsand the relationship between injection/production and resulting surface deformation, which also provides information about well-to-well connectivity. A neural network-based approach is followed to determine the nonlinear relationship between surface subsidence/dilation and injection-production. This is then used to build an objective function that works to minimize the differences between well-to-well subsidence/dilation measured by the tiltmeters, by adjusting injection-production for the wells.
In this paper, a method that harnesses real-time surface tiltmeter data to adjust injection-production distribution in diatomites to decrease well failures is used beyond the existing applications of surface tiltmeter, for instance, in the areas of detection of early steam breach to surface in steam operations and fracture orientation. This method also provides real-time data for robust reservoir management of such reservoirs where satellite imaging is not effective.
|File Size||5 MB||Number of Pages||41|
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