3D-static reservoir and basin modeling of a lacustrine fan-deltaic system in the Gulf of Suez, Egypt

This study provides insights into the experience gained from the investigation of the dynamic behavior of a mature sour hydrocarbon reservoir modeling under an acid gas re-injection process production strategy. The primary objective was to analyze and evaluate the production behavior of proposed injection zones by assessing various injection scenarios and obtaining oil production over time. To achieve that, a workflow was developed to prioritize potential injection areas, select the optimal wells, determine the optimal operational parameters and optimize a pilot application design based on expected performance. Within this framework, the study encompasses diverse acid gas injection schemes on a pilot scale approach, including acid gas combined with waterflooding. The outcome of this analysis will eventually lead to the identification of the most promising and highest-performing injection scheme, elucidating the optimal range of operating parameters. This optimal combination forms the basis for the economic analysis of the venture and the subsequent detailed design of a full-scale application, where real-world implementation will validate the projected results.

Section: Introductionmentioning

confidence: 99%

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

Kanakaki,

Samnioti,

Koffa

et al. 2023

“…A static reservoir model is a three-dimensional representation of a reservoir's geological properties, such as porosity, permeability and rock type. It is created using geological, well and seismic data together with a thorough interpretation, providing an approximate ''snapshot'' of a real reservoir at a specific time [3][4][5]. On the other hand, a dynamic reservoir model is a time-dependent simulation of fluid flow in a reservoir.…”

Section: Introduction 1reservoir Simulation In the Oil And Gas Industrymentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part I

Samnioti,

Gaganis

2023

In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time- and resource-associated costs, and rendering reservoir simulators is not fast or robust, thus introducing the need for more time-efficient and smart tools like ML models which can adapt and provide fast and competent results that mimic simulators’ performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for cases of individual simulation runs and history matching, whereas ML-based production forecast and optimization applications are presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

“…A static reservoir model is a three-dimensional representation of a reservoir's geological properties, such as the porosity, permeability, and rock type. It is created using geological, well, and seismic data together with a thorough interpretation, providing an approximate "snapshot" of the real reservoir at a specific time [4][5][6]. On the other hand, a dynamic reservoir model is a time-dependent simulation of fluid flow in the reservoir.…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II

Samnioti,

Gaganis

2023

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.