Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

Manrique, Eduardo; Izadi, Mehdi; Kitchen, Curtis David; Alvarado, Vladimir

doi:10.2118/113269-pa

Cited by 20 publications

(4 citation statements)

References 27 publications

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“…Two objective functions were used: For deterministic modeling, the profit-to-investment ratio was chosen, and for stochastic modeling, the objective function was the NPV. Manrique et al (2009) proposed a decision-making workflow that emphasizes the use of screening techniques in making business decisions in an EOR context. They presented two types of field cases: cases that had limited data and limited decision time, and cases that faced only limited-time scenarios.…”

Section: Decision-making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

2013

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This study introduces a decision-making evaluation method for flexibility in surfactant flooding. The method aims to capture the effects of uncertainty in the time series for both technical and economic parameters and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the least-squares Monte Carlo (LSM) method, which is best-suited for evaluating flexibility in project implementation. The decision analyzed was that of finding the best time to start surfactant flooding during the lifetime of a field under uncertainty. The study was conducted on two reservoir models: a 3D homogeneous model and a 2D heterogeneous model. The technical uncertainties considered were the residual oil saturation (ROS) to the surfactant flood, surfactant adsorption, and reservoir heterogeneity, and the main economic uncertain parameters considered were oil price, surfactant cost, and water-injection and -production costs. The results show that the LSM method provides a decision-making tool that was able to capture the value of flexibility in surfactant-flooding implementation and provides some insight into the effect of uncertainty on decision making, which can help mitigate adverse circumstances should they arise or capture the upside potential if circumstances prove beneficial. The results found that the optimal policy obtained was reliable and that heterogeneity and different well-placement patterns affect the value of flexibility and optimal policy for different reservoir models. Furthermore, possible extensions to enhance the LSM method were discussed.

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Section: Decision-making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

2013

View full text Add to dashboard Cite

show abstract

“…Two objective functions were used, for deterministic modelling the RPI (profit to investment ratio) was chosen and for stochastic modelling the objective function was the NPV. Manrique et al (2009) proposed a decision making workflow that emphasizes the use of screening techniques in making business decisions in an EOR context. It presented two types of field cases: cases which had limited data and limited decision time, and cases which faced only limited time scenarios.…”

Section: Decision Making Evaluation Processes For Surfactant Floodingmentioning

confidence: 99%

Decision Making Under Uncertainty in EOR: Applying the Least Squares MonteCarlo Method in Chemical EOR Implementation

Alkhatib

Babaei

King

2012

SPE Europec/Eage Annual Conference

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This study introduces a decision making evaluation method for flexibility in chemical EOR. The method aims to capture the effects of dynamic uncertainties both technical and economic and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the Least-Squares MonteCarlo(LSM) method which is best suited for evaluating flexibility in project implementation. The decision analysed was that of finding the best time to initiate surfactant flooding during the life time of a field under uncertainty. The study was conducted on two reservoir models: 3-D homogeneous model and a 2-D heterogeneous model. The technical uncertainties considered were the residual oil saturation to the surfactant flood, surfactant adsorption and reservoir heterogeneity while the main economic uncertain parameters considered were oil price, surfactant cost and water injection and production costs. The results show that the LSM method provides a decision making tool that was able to capture the value of flexibility in surfactant flooding implementation. The LSM method provides great insight into the effect of uncertainty on decision making which can help mitigate adverse circumstances should they arise. The results found that the optimal policy obtained was reliable and that heterogeneity does affect the optimal policy. It was also possible to consider the value of information using this method.

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“…The advanced method presented in details by Alvarado and Manrique (2010) and previously documented by Alvarado et al (2002), Manrique et al (2003), and Manrique et al (2009), is basically a representation in the form of two-dimensional expert maps of a database of successful EOR projects, indexed by the set of the six parameters listed above. These maps are obtained through a multidimensional data projection and allow qualitative identification reservoir clusters of EOR projects by visual inspection.…”

Section: Introductionmentioning

confidence: 99%

A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

Siena¹,

Milano²,

Guadagnini³

et al. 2015

Europec 2015

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We present and test a new screening methodology to discriminate amongst alternative and competing Enhanced Oil Recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques have been successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests prior to field-wide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Since similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria we consider fluid (density and viscosity) and reservoir formation (porosity, permeability, depth and temperature) properties. Our approach is observation-driven and grounded on an exhaustive data-base which we compile upon considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through Principal Component Analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian clustering algorithm. Considering the cluster which comprises the EOR field under evaluation, an inter-cluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components, and (b) the fraction of variance associated with each principal component is taken as weight of the Euclidean distance we determine. As a test bed, we apply our approach on three fields operated by eni. These include light, medium and heavy-oil reservoirs, where Gas, Chemical and Thermal EOR projects have been respectively proposed. Our results are (a) conducive to the compilation of a broad and extensively usable data-base of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.

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Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

Cited by 20 publications

References 27 publications

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

Decision Making Under Uncertainty in EOR: Applying the Least Squares MonteCarlo Method in Chemical EOR Implementation

A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

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