Kuparuk River Unit Field - The First 30 Years

Jensen, Trond B.; Little, Laird D.; Melvin, John D.; Reinbold, Eric W.; Jamieson, David Philip; Shi, Wenbin

doi:10.2118/160127-ms

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…We investigate immiscible gas injection because the reservoir pressure is much lower than MMP (Johns and Dindoruk, 2013). We assume that during the injection, the reservoir pressure does not significantly deviate from its initial value.…”

Section: Governing Equationsmentioning

confidence: 99%

“…The injection of gas, particularly CO 2 or the associated petroleum gas, into oil reservoirs allows for a significant increase in the microscopic displacement efficiency, E D , caused by the compositional exchanges between oil and gas, oil swelling and viscosity reduction (Lake, 1989;Blunt et al, 1993;Christensen et al, 2001;Thomas, 2008). As discussed by Pritchard and Nieman (1992), Sanchez (1999), Jensen et al (2012), and Johns and Dindoruk (2013), gas injection is often organised in the water-alternating-gas (WAG) process to also improve the volumetric sweep efficiency, E V . The total efficiency of oil recovery can be estimated as (Ghedan, 2009;Verma, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models

2021

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Abstract. We present a method for accelerated optimisation of CO2 injection into petroleum reservoirs. The optimisation assumes maximisation of the net present value by coupling reservoir models with the calculation of cash flows. The proposed method is based on the construction of a hierarchy of compositional reservoir models of increasing complexity. We show that in dimensionless volumes, the optimal water and gas slugs are very close for the 1-D and 2-D areal reservoir models of the water-alternating-gas (WAG) process. Therefore, the solution to the 1-D optimisation problem gives a good approximation of the solution to the 2-D problem. The proposed method is designed by using this observation. It employs a larger number of less computationally expensive 1-D compositional simulations to obtain a good initial guess for the injection volumes in much more expensive 2-D simulations. We suggest using the non-gradient optimisation algorithms for the coarse models on low levels of the hierarchy to guarantee convergence to the global maximum of the net present value. Then, we switch to the gradient methods only on the upper levels. We give examples of the algorithm application for optimisation of different WAG strategies and discuss its performance. We propose that 1-D compositional simulations can be efficient for optimising areal CO2 flooding patterns.

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Section: Governing Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models

2021

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Section: Targetmentioning

confidence: 99%

A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

Siena¹,

Guadagnini²,

Rossa

et al. 2016

SPE Reservoir Evaluation &Amp; Engineering

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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 intercluster 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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“…It can be noted that a hydrocarbon Miscible WAG (MWAG) injection project, i.e., the Kuparuk river field, in Alaska (US), is ranked as the highest analog to Target 3. Miscible injection at Kuparuk river started in 1988 and was expanded to a large scale since 1996, becoming one of the largest MWAG projects worldwide (Jensen et al, 2012). Although miscible gas injection is the primary EOR approach implemented in this field, chemical EOR methods also constitute a potentially viable alternative: laboratory tests and field trials have suggested low-salinity water injection to be a promising EOR technique within all of the Alaskan North Slope fields (McGuire et al, 2005, Llano et al, 2013.…”

Section: Cluster 2 (Targets 1 and 3)mentioning

confidence: 99%

A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

Siena¹,

Milano²,

Guadagnini³

et al. 2015

All Days

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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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Kuparuk River Unit Field - The First 30 Years

Cited by 11 publications

References 7 publications

Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models

Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models

A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

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Kuparuk River Unit Field - The First 30 Years

Cited by 11 publications

References 7 publications

Numerical optimisation of CO&lt;sub&gt;2&lt;/sub&gt; flooding using a hierarchy of reservoir models

Numerical optimisation of CO&lt;sub&gt;2&lt;/sub&gt; flooding using a hierarchy of reservoir models

A Novel Enhanced-Oil-Recovery Screening Approach Based on Bayesian Clustering and Principal-Component Analysis

A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

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Product

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Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models

Numerical optimisation of CO<sub>2</sub> flooding using a hierarchy of reservoir models