Miscible Gas Injection Piloting and Modeling in a Giant Carbonate
Reservoir

Haajizadeh, Masoud; Hafez, Hafez; Hassan, Rasheed Al; Katheeri, Abdulla Al; Ghozali, Muhamad; Kenawy, Maher

doi:10.2523/101473-ms

Cited by 2 publications

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“…These experimental data are needed to tune the EOS model of properly characterized fluid. Besides, simulation of 1-D slim tube is also computationally expensive since different simulation cases (at various pressure conditions) have to be carried out (Haajizadeh et al, 2006;Zhao, 2006;Ghomian, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Accuracy and Computational Time for Common and Artificial Methods in Predicting Minimum Miscibility Pressure

Ameri

Fard

Akbari

et al. 2013

Energy Exploration & Exploitation

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Minimum miscibility pressure is the least required pressure for complete mixing of gas and oil in the reservoir conditions. It is an important parameter in the processes of gas injection in a miscible manner and its precise determination is very vital in choosing the type of injecting gas and planning injecting equipment for increasing the recovery efficiency. The common method is determining MMP in slim tube or 1-D simulation of slim tube. Usually determining the minimum miscibility pressure via slim tube apparatus is an expensive and time-consuming test and to carry it out it is necessary to have a sample of reservoir oil and suggested injecting gas. Occasionally it is possible that for some unknown reasons despite spending much time and money it won't bring up any result. As a result, for determining this parameter, finding another method which has a higher precision in addition to being swift and less expensive is very necessary. On the other hand, there are several simulation methods to determine minimum miscibility pressure. These methods are so fast rather than slim tube experiment and relatively precise. MMP can be estimated numerically using compositional simulation, method of characteristics (MOC), mixing-cell methods, intelligent methods, and empirical correlations. However, nowadays one dimensional (1-D) slim tube simulation based on compositional simulation is very common. In this paper a suggestive method is proposed for determining MMP. A mixing rule method coupled with artificial neural network model (ANN) based on a numerous experiment data. Accuracy and computational time of artificial neural network method were compared to common prior models and correlations. The results show although intelligent methods are so fast, 1-D slim tube simulation is still a proper method to determine MMP in high accuracy. Average absolute relative error for MMP value is 1.5% for 1-D slim tube simulation, while the number for ANN is 3.25%. However, ANN method is recommended for fast MMP estimation.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Accuracy and Computational Time for Common and Artificial Methods in Predicting Minimum Miscibility Pressure

Ameri

Fard

Akbari

et al. 2013

Energy Exploration & Exploitation

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Challenges in Production Lift Performance Nodal Analysis for Miscible Enhanced Oil Recovery EOR Fields in Deep Water

Grego

2014

SPE Improved Oil Recovery Symposium

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Deep water oil and gas resources play an important part in the energy mix of today's world, and ultra deep water oil and gas production has been the frontier of the oil and gas industry for the past decades. As the technological advancements push the limits of the industry's capability in the realm of ultra deep water exploration and production, more and more complexities are involved in the technical developments of these fields. Some deep water fields are identified as candidates for enhance oil recovery (EOR) methods. Very recently, several major deep water projects around the globe are including a miscible gas flood in their field development plan. Miscible EOR, combined with extreme conditions such as high temperature, high pressure, ultra deep water and reservoir depths, poses brand new challenges in production engineering designs. This paper gives a summary of multiple challenges in the production lift performance for green field development of a gas miscible flood in deep water field. The authors have identified several key aspects in the nodal analysis methods that can potentially make significant impact on the accuracy of the predicted production lift performance for such fields. They include, among others, the characterization of fluid PVT (Pressure Volume and Temperature) and its implementation in nodal analysis tools, the use of fit-for-purpose vertical lift correlations, the implementation of compression constraints for gas injection, and the use of subsea pipeline rate multiplier as an approximation for subsea layout. Methodologies of resolving these challenges are proposed, and their limitations are discussed. This paper sheds light on improving the accuracy of nodal analysis of both injection and production for gas miscible EOR fields in deep water.

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Miscible Gas Injection Piloting and Modeling in a Giant Carbonate Reservoir

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Cited by 2 publications

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A Comparison of Accuracy and Computational Time for Common and Artificial Methods in Predicting Minimum Miscibility Pressure

A Comparison of Accuracy and Computational Time for Common and Artificial Methods in Predicting Minimum Miscibility Pressure

Challenges in Production Lift Performance Nodal Analysis for Miscible Enhanced Oil Recovery EOR Fields in Deep Water

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