Optimization of Oil Production Based on A Capacitance Model of Production and Injection Rates

Lake, Larry W.; Liang, Ximing; Edgar, Thomas F.; Al-Yousef, Ali; Sayarpour, Morteza; Weber, Daniel

doi:10.2118/107713-ms

Cited by 86 publications

(23 citation statements)

References 10 publications

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“…Fyrozjaee and Yortsos (5) considered the problem of optimizing and controlling a displacement front in a porous medium through the flow-rate partition in a horizontal well. Liang et al (6) adjusted water injection rates in a mature field through a capacitance model and used it for a nine-spot well pattern. Alhuthali et al (7) proposed a method to optimize water flooding by equalizing the arrival times of the waterflood front at all producers within selected sub-regions for a waterflood project.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Chen

Yang

2010

Journal of Canadian Petroleum Technology

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CO2 flooding has gained momentum in the oil and gas industry and might be suitable for approximately 80% of oil reservoirs worldwide based on the oil recovery criteria alone. In addition to miscibility, production performance needs to be optimized to achieve higher sweep efficiency and oil recovery. Although many techniques have been made available for production optimization in the upstream oil and gas industry, it is still a challenging task to optimize production performance in the presence of physical and/or financial uncertainties. In this paper, a new technique is developed to optimize production performance in a CO2 flooding reservoir under uncertainty. More specifically, potential uncertainties influencing production performance are analyzed and assessed by using the geostatistical technique. This enables us to integrate the available information within a unified and consistent framework and to generate multiple geological realizations accounting for uncertainty and spatial variability. Subsequently, the net present value (NPV) is selected as the objective function to be optimized by using the genetic algorithm, while well rates of the injectors and the flowing bottomhole pressure for the producers are chosen as the controlling variables. In addition, corresponding modifications have been made to accelerate the convergence speed of the genetic algorithm. A field case is used to demonstrate the procedures of the newly developed technique and the optimized results show that the oil recovery and the NPV can be increased by 6.4% and 9.2%, respectively. It is also found that the genetic algorithm is a powerful and reliable search method to optimize production performance of reservoirs with complex structures. Introduction CO2 flooding is considered as a promising and practical enhanced oil recovery (EOR) process because it not only increases oil recovery, but also reduces greenhouse gas emissions by sequestrating CO2 in the depleted reservoirs. In practice, CO2 flooding performance can be greatly affected by the reservoir heterogeneity, which can severely reduce the sweep efficiency, result in early CO2 breakthrough at the producers, and thus, leave a large amount of bypassed oil in the reservoir(1). Therefore, it is of fundamental and practical importance to optimize production performance of a CO2 flooding reservoir.

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Section: Introductionmentioning

confidence: 99%

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Chen

Yang

2010

Journal of Canadian Petroleum Technology

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“…The data set that we use to create the permeability field is the Stanford V data set (Mao and Journel 1999), which provides a quasiexhaustive sampling of petrophysical properties over multiple layers. An open-source computer package (Stanford geostatistical modeling software or SGEMS) is used to perform sequential Gaussian simulation (Nowak and Verly 2005) to generate different permeability realizations.…”

Section: Synthetic Case Studymentioning

confidence: 99%

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

Cao

Luo

Lake

2015

SPE Reservoir Evaluation &Amp; Engineering

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Many empirical and analytical models were developed to forecast oil production. Empirical models (including data-driven models) can, for example, find correlations between oil cut and production, but they lack explicit knowledge of the physical behavior. Classic analytical models are loyal to reservoir physics. Nevertheless, they often require estimation of water saturation as a function of time, which is difficult to obtain for multiwell reservoirs. It is desirable to combine advantages of both empirical and analytical models and develop a physical-model-based method that uses field data to infer oil rate.In this paper, we propose to infer fractional-flow models from field data by use of the Koval (1963) theory. We inversely solved the Koval fractional-flow equation to obtain a relationship between water cut and dimensionless time. By history matching field water-cut data, two model parameters, the Koval factor and the producer-drainage volume, are estimated. Nevertheless, it is challenging to use the Koval approach as a predictive model directly because the injection contribution into each producer in a future-time horizon must be evaluated first.To address this issue, we combine the Koval approach with the capacitance/resistance model (CRM), which characterizes the injector/producer connectivities and response time. The material balance of fluids is established in a producer-based drainage volume to consider the contributions from nearby injectors and the time lag in production caused by reservoir/fluids compressibility. A regression approach is simultaneously advanced to minimize the model error. Because of robustly integrating the reservoir physical behavior and the data-driven approach, the combination of the Koval theory and the CRM can result in a synergy that leads to accurate oil-rate predictions.We validated this integrated method in synthetic homogeneous and heterogeneous reservoirs to test its reliability, and further applied it to a field case in western Venezuela. Case studies demonstrate that one can use this integrated model as a real-time tool to characterize interwell connection and to predict future oil production accurately.

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“…A version of the CM, called the CMP, uses the producer drainage volume based CM (CMP, Liang et al, 2007;Liang, 2010). For the CMP, ij ϭ j and kj ϭ j .…”

Section: The Modified Capacitance Model With Pseudo Wellmentioning

confidence: 99%

Determining Well-to-Well Connectivity in Tight Reservoirs

Mirzayev

Riazi

Cronkwright

et al. 2015

Day 1 Tue, October 20, 2015

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Drilling and fracture stimulating horizontal wells in economically marginal fields, such as tight oil reservoirs, is a common practice. Estimating the connectivity between these wells (IWC) is important for the design and management of a field when waterflood recovery is applied. For example, IWC affects the determination of fracture conductivity and length and can help identify favorable locations for infill drilling and optimize injection rates.Numerical simulators, such as streamline simulation need a geological model to determine IWC. Constructing the geological model, however, may take months. In this paper, we show how an alternative method, developed for and successfully applied in conventional reservoirs to determine IWC, can be modified to estimate tight formation IWC using solely production-and injection-rate fluctuations. The model is based on the Capacitance Model, or CM, and is modified to include a pseudo well (CM-PW).The CM-PW is tested with various heterogeneous simulation cases and field data. The CM-PW can identify inter-well heterogeneities and differentiate them spatially. We observed that in some cases the CM-PW performs better than streamline simulation results while calculating production allocation factors. The CM-PW detects whether all the injected fluid volume goes to producers. The model accuracy is based on the match to production data and, for all simulation cases, the model accuracy R 2 Ն 0.83. The field application, from a tight oil waterflood in the Bakken Formation, shows that the CM-PW results agree with tracer test results and field reports showing fracture hits between wells. Seismic interpretations also strongly support the CM-PW evaluations.The IWCs evaluated by the CM-PW during unsteady state flow periods provide key information to make a favorable decision at the early stage of water flooding in tight oil formations. Integrating the connectivity analysis, with tracer tests, seismic, and the geology enabled us to determine the reasons for the connectivity observed, identifying whether injected fluid flows through geological bodies or fractures between an injector-producer pair.

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Optimization of Oil Production Based on A Capacitance Model of Production and Injection Rates

Cited by 86 publications

References 10 publications

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Optimization of Production Performance in a CO2 Flooding Reservoir Under Uncertainty

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

Determining Well-to-Well Connectivity in Tight Reservoirs

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