Application of Ensemble Variance Analysis in the Development of the Wheatstone Field Startup Strategy
- Matthew Flett (Chevron Australia) | Paul Connell (Chevron Australia)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2019
- Document Type
- Journal Paper
- 2019.Society of Petroleum Engineers
- Wheatstone, interference testing, ensemble variance analysis
- 21 in the last 30 days
- 72 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Starting initial field production is a unique opportunity to acquire reservoir-surveillance information that can impact future reservoir performance. When a field is perturbed by first production, there is the potential to reduce uncertainty in reservoir properties by monitoring the pressure changes at nonproducing wells with downhole pressure gauges and comparing the observed signal with a range of simulation-model results.
The Wheatstone Field, located offshore of Northwest Australia, recently began production startup to supply gas to the Wheatstone liquefied-natural-gas (LNG) facility. The operational guidelines required each development well to start with a single-well-cleanup flow to the Wheatstone platform. The initial single-well-cleanup flows of the Wheatstone field provided scope for selecting a well flow sequence with observation at nonproducing wells.
The recommended sequence of initial cleanup flows was designed with a focus on reducing reservoir uncertainties by means of ensemble variance analysis (EVA). EVA is a statistical correlation technique that compares the covariance between two sets of output data with the same set of inputs. For the Wheatstone Field well-cleanup flow sequence selection, the EVA workflow compared the full-field design of experiments (DoE) study of field depletion and a series of short early-production reservoir-simulation DoE studies of the gas field. The covariance between the two DoE studies was evaluated. The objective of the EVA approach was to determine the startup sequence that would allow for the best opportunity to reduce subsurface uncertainty. This objective was met by ranking multiple cleanup flow sequence scenarios. The key factors considered for sequence selection ranking were the impact on business objectives, such as future-drilling-campaign timing and location of infill wells, and insights on reservoir connectivity, gas initially in place (GIIP), and permeability.
The recommended sequence of well-cleanup flows uses a superpositioned pressure signal to boost response at observation wells, which improves measurement resolvability. The selected sequence preserves key observation wells for each manifold and reservoir section for as long as possible before those wells are required to be flowed to meet operational requirements. Operational constraints and variations of the startup plan were considered part of the evaluation.
|File Size||1 MB||Number of Pages||11|
Flett, M. and Muller, M. 2016. Early Field Life Interference Pulse Test Design To Refine Reservoir Uncertainties: A Reservoir Surveillance Opportunity for the Wheatstone Gas Field, Australia. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 25–27 October. SPE-182325-MS. https://doi.org/10.2118/182325-MS.
He, J., Sarma, P., Bhark, E. et al. 2017a. Quantifying Value of Information Using Ensemble Variance Analysis. Presented at the SPE Reservoir Simulation Conference, Montgomery, Texas, 20–22 February. SPE-182609-MS. https://doi.org/10.2118/182609-MS.
He, J., Tanaka, S., Wen, X.-H. et al. 2017b. Rapid S-Curve Update Using Ensemble Variance Analysis With Model Validation. Presented at the SPE Western Regional Meeting, Bakersfield, California, 23–27 April. SPE-185630-MS. https://doi.org/10.2118/185630-MS.
He, J., Sarma, P., Bhark, E. et al. 2018. Quantifying Expected Uncertainty Reduction and Value of Information Using Ensemble-Variance Analysis. SPE J. 23 (2): 428–448. SPE-182609-PA. https://doi.org/10.2118/182609-PA.
Swarbrick, T. and Muller, M. 2016. Innovative Experimental Design Workflows for Coupled Models. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Perth, Australia, 25–27 October. SPE-182340-MS. https://doi.org/10.2118/182340-MS.