Optimizing Reservoir Performance Under Uncertainty with Application to Well Location

Aanonsen, Sigurd Ivar; Eide, Asle; Holden, Lars; Aasen, J.O.

doi:10.2118/30710-ms

Cited by 57 publications

(15 citation statements)

References 3 publications

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“…A mathematical regression model is then created for each of the production responses (oil recovery, plateau period, etc.). The model, also called "Response Surface Model" or RSM 3,4 , is then used for generating probabilistic distribution of each response using the "Monte Carlo" technique. The final results are P90, P50 and P10 values of the "production responses.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty Analysis of a Fractured Carbonate Reservoir

Al-Thuwaini

Alalwan

Ranjan³

2009

SPE Middle East Oil and Gas Show and Conference

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State of the art methodology using experimental design and response surface techniques were used to perform uncertainty and risk analysis for managing reservoir uncertainties during history matching, production forecasting and production scheme optimization.In this paper we present the method applied successfully on a complex carbonate fractured reservoir with a long production history with approximately 500 producers and injectors. The simulation model is dual porosity/dual permeability, with 5 million cells. The objective is to quantify the impact of principle reservoir uncertainties on key field performance responses such as cumulative oil production, plateau length and water production using a risk analysis approach. The main uncertainties of the field were fracture connectivity, matrix-to fracture transfer, super-permeability streaks (Super-K) extent and matrix properties in a well spacing of one kilometer.From field performance experience, main parameters were identified and their responses during the historical production period were analyzed. The impact of parameters on history match quality was analyzed and an appropriate range of uncertainty was selected. A sensitivity analysis was performed to prioritize and rank the key influential parameters impacting production forecast. A novel methodology was applied to convert discontinuous realizations into a more continuous problem. Using the most influential selected parameters, another sensitivity analysis was done to accurately predict the dispersion in the production responses. A polynomial regression equation was derived for each of the main production responses. Finally, probabilistic distributions were generated for key production responses. This paper will highlight results of the study that helped in giving clear direction in field development planning by identifying and quantifying the main drivers influencing production behavior. Additionally, the results allow for quantifying the key uncertainty of the model forecast and associated risk to be investigated further. The results of the study can also be used for production optimization process by deriving the best operating constraints for highest recovery.

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Section: Methodsmentioning

confidence: 99%

Uncertainty Analysis of a Fractured Carbonate Reservoir

Al-Thuwaini

Alalwan

Ranjan³

2009

SPE Middle East Oil and Gas Show and Conference

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“…This is accomplished by comparing the randomly generated locations with the points existing in the database. This method is similar to the kriging proxy employed by some researchers [6] [14] [15]. The exception is that, the database of points (proxy) in our case is not used to replace the simulator when estimating for un-simulated points as is done with the kriging proxy.…”

Section: Dealing With Out-of-boundary-pointmentioning

confidence: 99%

Algorithms for the Optimization of Well Placements—A Comparative Study

Udoeyop¹,

Oboh²,

Afiakinye³

2018

ACES

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The Artificial Bee Colony (ABC) is one of the numerous stochastic algorithms for optimization that has been written for solving constrained and unconstrained optimization problems. This novel optimization algorithm is very efficient and as promising as it is; it can be favourably compared to other optimization algorithms and in some cases, it has been proven to be better than some known algorithms (like Particle Swarm Optimization (PSO)), especially when used in Well placement optimization problems that can be encountered in the Petroleum industry. In this paper, the ABC algorithm has been modified to improve its speed and convergence in finding the optimum solution to a well placement optimization problem. The effects of variations of the control parameters for both algorithms were studied, as well as the algorithms' performances in the cases studied. The modified ABC (MABC) algorithm gave better results than the Artificial Bee Colony algorithm. It was noticed that the performance of the ABC algorithm increased with increase in the number of its optimization agents for both algorithms studied. The modified ABC algorithm overcame the challenge posed by the use of uniformly generated random numbers with very rough NPV surface. This new modified ABC algorithm proposed in this work will be a great tool in optimization for the Petroleum industry as it involves Well placements for optimum oil production.

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“…A survey of these methods is presented in the following: D-optimality experimental design: This method was first proposed by Aanonsen et al (1995) in well placement optimization under uncertainty. They included the effect of uncertainty in reservoir description using a combination of Doptimality experimental design technique and response surface methodology.…”

Section: Effect Of Uncertaintymentioning

confidence: 99%