IOR Pilot Evaluation in a Brown-Field Fractured Reservoir Using Data Analytics of Reservoir Simulation Results
- Jincong He (Chevron ETC) | Robin Hui (Chevron ETC)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Simulation Conference, 10-11 April, Galveston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 5.5.8 History Matching, 5.8.6 Naturally Fractured Reservoir, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment, 5 Reservoir Desciption & Dynamics, 5.8 Unconventional and Complex Reservoirs, 5.4.1 Waterflooding, 5.4 Improved and Enhanced Recovery
- Pilot evaluation, Classification and regression tree, Ensemble variance analysis, Data space inversion, Value of information
- 6 in the last 30 days
- 218 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
A well-designed pilot is instrumental in reducing uncertainty for the full-field implementation of improved oil recovery (IOR) operations. Traditional model-based approaches for brown-field pilot analysis can be computationally expensive as it involves probabilistic history matching first to historical field data and then to probabilistic pilot data. This paper proposes a practical approach that combines reservoir simulations and data analytics to quantify the effectiveness of brown-field pilot projects.
In our approach, an ensemble of simulations are first performed on models based on prior distributions of subsurface uncertainties and then results for simulated historical data, simulated pilot data and ob jective functions are assembled into a database. The distribution of simulated pilot data and ob jective functions are then conditioned to actual field data using the Data-Space Inversion (DSI) technique, which circumvents the difficulties of traditional history matching. The samples from DSI, conditioned to the observed historical data, are next processed using the Ensemble Variance Analysis (EVA) method to quantify the expected uncertainty reduction of ob jective functions given the pilot data, which provides a metric to ob jectively measure the effectiveness of the pilot and compare the effectiveness of different pilot measurements and designs. Finally, the conditioned samples from DSI can also be used with the classification and regression tree (CART) method to construct signpost trees, which provides an intuitive interpretation of pilot data in terms of implications for ob jective functions.
We demonstrate the practical usefulness of the proposed approach through an application to a brown-field naturally fractured reservoir (NFR) to quantify the expected uncertainty reduction and Value of Information (VOI) of a waterflood pilot following more than 10 years of primary depletion. NFRs are notoriously hard to history match due to their extreme heterogeneity and difficult parameterization; the additional need for pilot analysis in this case further compounds the problem. Using the proposed approach, the effectiveness of a pilot can be evaluated, and signposts can be constructed without explicitly history matching the simulation model. This allows ob jective and efficient comparison of different pilot design alternatives and intuitive interpretation of pilot outcomes. We stress that the only input to the workflow is a reasonably sized ensemble of prior simulations runs (about 200 in this case), i.e., the difficult and tedious task of creating history-matched models is avoided. Once the simulation database is assembled, the data analytics workflow, which entails DSI, EVA, and CART, can be completed within minutes.
To the best of our knowledge, this is the first time the DSI-EVA-CART workflow is proposed and applied to a field case. It is one of the few pilot-evaluation methods that is computationally efficient for practical cases. We expect it to be useful for engineers designing IOR pilot for brown fields with complex reservoir models.
|File Size||1 MB||Number of Pages||18|
Hui M., Dufour G., Vitel S., Muron P., Tavakoli R., Rousset M., Rey A., and Mallison B. 2019. A robust embedded discrete fracture modeling workflow for simulating complex processes in field-scale fractured reservoirs. SPE Reservoir Simulation Conference, Galveston, Texas, USA, April 10-11, Society of Petroleum Engineers.
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., and Duchesnay E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12: 2825–2830.