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Abstract

Summary

Improving hydrocarbon recovery from green and mature fields through targeting potential drilling requires a computationally complex process of well placement optimization. Because these operational activities are expensive and particularly critical in the periods of low oil prices, a risk quantification analysis is often required for uncertainty considerations.

A new automated probabilistic workflow optimizes well placement using probability maps based on reservoir and simulation opportunity indexes. Well types include vertical wells and horizontal wells. The probability map concept aims at unifying the existing model realizations into a single probability map by establishing thresholds for key physical parameters and reservoir characteristics. The opportunity indexes method is a fast way to identify zones with high potential for production from both oil and gas reservoirs.

The workflow is generic and can be applied to oil and gas in both mature and green fields as a fast method of well placement optimization under uncertainty. Starting from a set of reservoir modelling scenarios, a pattern recognition algorithm is first applied to classify and rank the realizations. A representative subset of these realizations is then used in an ensemble-based method and, when needed, calibrated to existing observed data. Reservoir and simulation opportunity indexes are applied to all calibrated realizations. Finally, a single probability map is created to unify the opportunity index maps. Based on the pattern observed in the probability map, areas of interest (AOI) are outlined, and several realizations of well configurations are generated. The designed wells are screened based on engineering criteria and ultimately assessed using numerical simulations on each realization.

The workflow terminates by results analysis and well design selection. This considers not only the improvement in oil recovery, but also a measure of risk coming from the uncertainty assessment. In testing on several simulation models, the unswept reservoir regions were successfully identified and ranked for drilling targets. Compared to existing methods, the workflow showed superior results.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201802212
2018-09-03
2024-04-28

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References

  1. Abdy, Y., Amari, M., Sharifzadeh, A., Al-Anzi, E., and Al-Ansari, M.
    [2012] An Integrated and Effective Method for Well Placement Using a Numerical Simulation Model: A North Kuwait Field Case Study. SPE Kuwait International Petroleum Conference and Exhibition, SPE-163378-MS.
     [Google Scholar]
  2. Al-Khazraji, A. K. and Shuker, M. T.
    ; Development of Heterogeneous Immature Brownfield with Waterdrive Using Dynamic Opportunity Index: A Case Study from Iraqi Oilfields. SPE North Africa Technical Conference and Exhibition, SPE-175708-MS.
     [Google Scholar]
  3. Al-Sada, A. I., Bouaouaja, M. T., Al-Hhuthali, A. H., and Al-Safi, A. A.
    [2014] Sweet Spot Identification and Optimum Well Planning: an Integrated Workflow to Improve the Sweep in a Sector of Giant Carbonate Mature Oil Reservoir. SPE Saudi Arabia Section Technical Symposium and Exhibition, SPE-172210-MS.
     [Google Scholar]
  4. Ataei, A., Soni, S., Chuah, B., and Yeek, H. H.
    [2014] Reservoir Opportunity Index – Advance in Well and Subsurface Design for Cost Effective Field Development. SPE Asia Pacific Oil & Gas Conference and Exhibition, SPE-171460-MS.
     [Google Scholar]
  5. Dossary, M., Al-Turki, A., Harbi, B., and Faleh, A.
    [2017] Progressive-Recursive Self-Organizing Maps PR-SOM for Identifying Potential Drilling Target Areas. SPE Middle East Oil & Gas Show and Conference, SPE-183803-MS.
     [Google Scholar]
  6. Ghazali, M. G. A. And Raub, M.R.B.A.
    [2011] Optimizing Development Strategy and Maximizing Field Economic Recovery through Simulation Opportunity Index. SPE Reservoir characterisation and Simulation Conference and Exhibition, SPE-148103-MS.
     [Google Scholar]
  7. Güyagüler, B. and Horne, R. N.
    [2001] Uncertainty Assessment of Well Placement Optimization. SPE Annual Technical Conference and Exhibition, SPE-71625-MS.
     [Google Scholar]
  8. ISAPP
    [2018] Olympus Challenge. ISAPP Integrated Systems Approach for Petroleum Production. http://www.isapp2.com/optimization-challenge.html, accessed on 1 May 2018.
     [Google Scholar]
  9. Molina, A. R. and Rincon, A. A.
    [2009] Exploitation Plan Design Based on Opportunity Index Analysis in Numerical Simulation Models. Presented at the Latin American and Caribbean Petroleum Engineering Conference, SPE-122915-MS.
     [Google Scholar]
  10. Montes, G., Bartolome, P.; Udias, A. L.
    [2001] The Use of Genetic Algorithms in Well Placement Optimization, SPE Latin American and Caribbean Petroleum Engineering Conference, SPE-69439.
     [Google Scholar]
  11. Mustapha, H., Chatterjee, S., Dimitrakopoulos, R.
    [2011] Geologic heterogeneity representation using high-order spatial cumulants for subsurface flow and transport simulations, Water Resources Research47 (8) 16.
     [Google Scholar]
  12. [2014] CDFSIM: efficient stochastic simulation through decomposition of cumulative distribution functions of transformed spatial patterns, Mathematical Geosciences46 (1), 95–123
     [Google Scholar]
  13. Mustapha, H., Dimitrakopoulos, R.
    [2010a] High-order stochastic simulation of complex spatially distributed natural phenomena, Mathematical Geosciences42 (5), 457–485.
     [Google Scholar]
  14. [2010b] High-order stochastic simulations for complex non-Gaussian and non-linear geological patterns, Mathematical Geosciences42 (5), 457–485
     [Google Scholar]
  15. [2010c] Generalized Laguerre expansions of multivariate probability densities with moments, Computers & Mathematics with Applications60 (7), 2178–2189
     [Google Scholar]
  16. [2011] HOSIM: A high-order stochastic simulation algorithm for generating three-dimensional complex geological patterns Computers & geosciences37 (9), 1242–1253.
     [Google Scholar]
  17. Saputra, W.
    [2016] Simulation Opportunity Index, A Simple and Effective Method to Boost the Hydrocarbon Recovery. SPE Annual Technical Conference and Exhibition, SPE-184501-STU.
     [Google Scholar]
  18. Varela-Pineda, A., Hutheli, A. H., and Mutairi, S. M.
    [2014] Development of Mature Fields Using Reservoir Opportunity Index: A Case Study from a Saudi Field. SPE Saudi Arabia Section Technical Symposium and Exhibition, SPE-172231-MS.
     [Google Scholar]
  19. Wang, H., Echeverria-Ciaurri, D., Durlofsky, L., and Cominelli, A.
    [2012] Optimal Well Placement Under Uncertainty Using a Retrospective Optimization Framework. SPE Reservoir Simulation Symposium, SPE-141950-PA.
     [Google Scholar]
  20. Yuen, B., Rashid, O., Al-Shammari, M., Al-Ajmi, F., Pham, T., Rabah, M., and Moreno, J. C.
    [2011] Optimizing Development Well Placements Within Geological Uncertainty Utilizing Sector Models. SPE Reservoir Characterisation and Simulation Conference and Exhibition, SPE-148017-MS.
     [Google Scholar]
  21. Zandvliet, M.J., Handels, M., van Esse, G. M., Brouwer, D. R., and Jansen, J. D.
    [2007] Adjoint-Based Well-Placement Optimization Under Production Constraints. SPE Reservoir Simulation Symposium, SPE-105797-MS.
     [Google Scholar]
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Well Placement Optimization Under Uncertainty Using Opportunity Indexes Analysis And Probability Maps
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802212
/content/papers/10.3997/2214-4609.201802212
/content/papers/10.3997/2214-4609.201802212
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