Pressure and Rate Rebalancing to Improve Recovery in a Miscible CO2 EOR Project

Linroth, Mark; Rickard, Andrew E.

doi:10.2118/169175-ms

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…KM's fully-miscible CO 2 flood in the SACROC unit expanded phase-by-phase from the central area outward, after it began with injection into a limited area. These phases are referred to as Љexpansion areasЉ [5]. Table 2 and Figure 8 summarize a timeline of the SACROC unit re-development from 1996 to 2008.…”

Section: Recent Developmentsmentioning

confidence: 99%

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Overview of CO2 Injection and WAG Sensitivity in SACROC

Ghahfarokhi

Pennell²,

Matson³

et al. 2016

SPE Improved Oil Recovery Conference

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This paper presents an overview of the SACROC Unit's activity focusing on different CO 2 injection and WAG projects that have made the SACROC Unit one of the most successful CO 2 injection projects in the world. The main objective of this work was to review CO 2 injection and injection rate losses due to the CO 2 /WAG miscible displacement process in the SACROC Unit and recommend an injection strategy for WAG-sensitive patterns.Two types of pattern CO 2 /WAG injection rate performance were observed, 1) WAG-sensitive and 2) WAG insensitive. WAG-sensitive patterns displayed loss of CO 2 injectivity, exceeding 80% in some patterns, during water-alternating-gas (WAG) injection, and an apparent reduction in water injectivity during the follow-up brine injection. This injectivity loss was observed in over 150 injection patterns. Over time, CO 2 injectivity tended to return to prior-to-WAG values. WAG-insensitive patterns suffer from these injectivity losses and were characterized by differences in 1) injectivity profiles, 2) Dykstra-Parsons coefficients, and 3) injectivity indexes.In the majority of WAG-sensitive patterns, injectivity profiles redistributed after CO 2 injection, while WAG-insensitive patterns did not show a significant change in their injectivity profiles over time. In a limited data set, the mean Dykstra-Parsons coefficient calculated for WAG-sensitive patterns was 0.83, while for WAG-insensitive patterns the mean Dykstra-Parsons coefficient was 0.76. However it was observed that in the lower Dykstra-Parsons patterns (WAG-insensitive patterns) much larger injectivity indexes were also observed; 19.5 bbl/day/psi, compared to 8.5 bbl/day/psi for higher Dykstra-Parsons patterns. This suggests that the WAG-insensitive patterns were dominated by fracture flow rather than matrix flow. These observations indicate that the WAG injection process in these heterogeneous SA-CROC wells is successful in diverting the injected fluids from zones with higher permeability to zones with lower permeability.For wells with injectivity values of less than 10 bbl/day/psi it is recommended to begin CO 2 /WAG injection with a long CO 2 cycle since they are likely to show sensitivity to WAG.A simulated 5-spot pattern was used to study the injection schedule for WAG-sensitive patterns. Longer CO 2 cycles and shorter water cycles improved the injectivity and pattern production. Most importantly, it was observed that increasing producing BHP to MMP resulted in significantly lower GOR.

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Section: Recent Developmentsmentioning

confidence: 99%

“…Of these, the GZ shows (a) the highest matrix permeability, (b) significant non-matrix flow features, and (c) high conductivity channels. Moreover, the transition zone (TZ) below the oil-water contact has recently been developed in parts of the SACROC unit [1,5].…”

Section: Introductionmentioning

confidence: 99%

Overview of CO2 Injection and WAG Sensitivity in SACROC

Ghahfarokhi

Pennell²,

Matson³

et al. 2016

SPE Improved Oil Recovery Conference

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Improving Recovery in the Yates Field Using Dynamic Feedback Loop based on Physics-Informed Artificial Intelligence

Bruyker¹,

Kosut

Valdéz³

et al. 2020

Day 2 Tue, September 01, 2020

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We present a robust control system and methodology for physics-informed artificial intelligence (PAI) used to optimize and improve oil recovery, demonstrated in the Yates Field operated by the Kinder Morgan CO2 company. The system consists of a robust control system (referred to as Dynamic Feedback Loop, or DFL) equipped with novel hydrocarbon sensors that measure oil concentration and other parameters continuously and simultaneously on a set of producing wells. The goal of this system is to optimize operational parameters (e.g. choke valve settings, injection rates) to reach specific target metrics of production (e.g. maximizing produced oil while minimizing produced gas). The key element of our approach is the use of a multi-layer artificial neural network (deep neural network, or DNN, to be specific) that extracts physics-based parameters from the real-time measurements and predicts relevant parameters of the DFL control system. DNNs are prone to overfitting in training, making them ineffective in unfamiliar or challenging situations outside the training dataset. To overcome this problem, we have developed a physics-informed robust neural network technique, where the reservoir physics and sensor data are used to train DNN representations of the key physical parameters. Typically, only simplified physical models are developed using available geostatic or historical production data. Also, due to the dynamic nature of these systems, the accuracy of the models often changes over time. To improve predictive capability of the model, we combine the DNN with the system-theoretic robust control concepts based on physics with a model uncertainty formulation. The concept was first validated using a combination of simulations, isolated sensor data and analyses based on sets of historic production data. A study using historic production data on Kinder Morgan's Yates Field Unit (YFU) 4045 Pilot (3 producing wells) indicates application of the DFL system results in an increase in cumulative production of up to 35% per year, compared to what is obtained through a traditional (fixed-point) control system. Currently, the DFL is being field-tested on a different set of wells in the Yates Field, instrumented with the novel hydrocarbon sensors that generate continuous and simultaneous production data.

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Miscible Displacement Processes

2018

Enhanced Oil Recovery

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Pressure and Rate Rebalancing to Improve Recovery in a Miscible CO2 EOR Project

Cited by 4 publications

References 5 publications

Overview of CO2 Injection and WAG Sensitivity in SACROC

Overview of CO2 Injection and WAG Sensitivity in SACROC

Improving Recovery in the Yates Field Using Dynamic Feedback Loop based on Physics-Informed Artificial Intelligence

Miscible Displacement Processes

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