Prospects of an Acid Gas Re-Injection Process into a Mature Reservoir

Kanakaki, Eirini Maria; Samnioti, Anna; Koffa, Evangelia; Dimitrellou, Irene; Obetzanov, Ivan; Tsiantis, Yannis; Kiomourtzi, Paschalia; Gaganis, Vassilis; Stamataki, Sofia

doi:10.3390/en16247989

Cited by 3 publications

(1 citation statement)

References 44 publications

(67 reference statements)

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“…Subsequently, the GPS optimizer was tested on a reservoir oil with a H 2 S concentration of 31.43%. Note that this composition represents the reservoir fluid of an oil field in Northern Greece [31,32], the high H 2 S content of which poses a significant challenge in EoS model tuning due to the polar nature of H 2 S. The developed EoS model will be utilized for simulating an Enhanced Oil Recovery (EOR) process that includes acid gas (primarily comprising H 2 S and CO 2 ) injection. According to the workflow outlined in Section 2, the heavy end was split into single carbon number (SCN) fractions which were further lumped into three pseudo-components to maintain the number of components as low as possible.…”

Section: H 2 S-rich Oilmentioning

confidence: 99%

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

Kanakaki,

Gaganis

2024

Liquids

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A computational model that can accurately describe the thermodynamics of a hydrocarbon system and its properties under various conditions is a prerequisite for running reservoir and pipeline simulations. Cubic Equations of State (EoS) are mathematical tools used to model the phase and volumetric behavior of reservoir fluids when compositional effects need to be considered. To anticipate uncertainty and enhance the quality of their predictions, EoS models must be adjusted to adequately match the available lab-measured PVT values. This task is challenging given that there are many potential tuning parameters, thus leading to various tuning results of questionable validity. In this paper, we present an automated EoS tuning workflow that employs a Generalized Pattern Search (GPS) optimizer for efficient tuning of a cubic EoS model. Specifically, we focus on the Peng–Robinson (PR) model, which is the oil and gas industry standard, to accurately capture the behavior of diverse multicomponent, complex hydrocarbon mixtures encountered in subsurface reservoirs. This approach surpasses the limitations of conventional gradient-based (GB) methods, which are susceptible to getting trapped in local optima. The proposed technique also allows physical constraints to be imposed on the optimization procedure. A gas condensate and an H2S-rich oil were used to demonstrate the effectiveness of the GPS algorithm in finding an optimized solution for high-dimensional search spaces, and its superiority over conventional gradient-based optimization was confirmed by automatically tracking globally optimal and physically sound solutions.

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Section: H 2 S-rich Oilmentioning

confidence: 99%

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

Kanakaki,

Gaganis

2024

Liquids

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Mass Balance-Based Quality Control of PVT Results of Reservoir Oil DL Studies

Papanikolaou,

Kanakaki,

Lempesis

et al. 2024

Energies

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Fluid properties live at the heart of hydrocarbon reservoir engineering as it is related to the behavior of fluids under reservoir pressure depletion. To obtain their values, PVT experimental work needs to be performed. Although the experimental workflow is strictly defined, the accuracy of the measurements often suffers from significant errors which in turn affect severely all the following engineering calculations as well as the following equation of state (EoS) tuning step. In this work, a systematic methodology is developed to apply quality control (QC) on the PVT values reported in a typical lab report. Firstly, the equations expressing mass balance are developed to calculate the missing closing data, such as the residual oil density and the composition of oil collected at each stage of the depletion study. Subsequently, computational tools are developed to evaluate the physical soundness of the received results and provide insight as to whether the quality of the experimental data is sufficient. To demonstrate the proposed workflow, it is applied to a selection of representative reservoir fluids of varying volatility. We demonstrate that depending on the reservoir fluid properties, calculations may yield highly unrealistic results, which engineers should avoid using unless corrected. Specifically, differential liberation (DL) test properties reported for low-volatility oils are generally reliable. However, for high-volatility oils, particularly during the final depletion stages, the test outcomes often become unrealistic. Finally, instructions to fluid and reservoir engineers on how to handle those issues and protect the reliability of their calculations are provided.

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Accelerating Numerical Simulations of CO2 Geological Storage in Deep Saline Aquifers via Machine-Learning-Driven Grid Block Classification

Kanakaki,

Ismail,

Gaganis

2024

Processes

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The accurate prediction of pressure and saturation distribution during the simulation of CO2 injection into saline aquifers is essential for the successful implementation of carbon sequestration projects. Traditional numerical simulations, while reliable, are computationally expensive. Machine learning (ML) has emerged as a promising tool to accelerate these simulations; however, challenges remain in effectively capturing complex reservoir dynamics, particularly in regions experiencing rapid changes in pressure and saturation. This article addresses the challenges by introducing a fully automated, data-driven ML classifier that distinguishes between regions of fast and slow variation within the reservoir. Firstly, we demonstrate the variability in pressure across different reservoir grid blocks using a simple brine injection and production scenario, highlighting the limitations of conventional acceleration approaches. Subsequently, the proposed methodology leverages ML proxies to rapidly and accurately predict the behavior of slow-varying regions in CO2 injection simulations, while traditional iterative methods are reserved for fast-varying areas. The results show that this hybrid approach significantly reduces the computational load without compromising on accuracy. This provides a more efficient and scalable solution for modeling CO2 storage in saline aquifers.

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Prospects of an Acid Gas Re-Injection Process into a Mature Reservoir

Cited by 3 publications

References 44 publications

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

Automated Equations of State Tuning Workflow Using Global Optimization and Physical Constraints

Mass Balance-Based Quality Control of PVT Results of Reservoir Oil DL Studies

Accelerating Numerical Simulations of CO2 Geological Storage in Deep Saline Aquifers via Machine-Learning-Driven Grid Block Classification

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