Temizel, Cenk (Area Energy) | Nabizadeh, Mehdi (International Petro Asmari Company) | Kadkhodaei, Nematollah (International Petro Asmari Company) | Ranjith, Rahul (University of Southern California) | Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Dhannoon, Diyar (Texas A&M University)
Decision making in waterflooding operations is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. This study investigates the reservoir management of waterfloods in terms of injection/production practices. A well-organized historical database that also collects real-time data is especially important in utilization of data-driven methods in the process of determination of optimum injection/production practices for waterfloods that will result in better recovery and sweep, which is illustrated in this paper.
Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to the lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding for improved decisions and results. Historical injection and production data at pattern level is utilized to determine the optimum injection levels in light of significant factors that affect the success of a waterflooding displacement process with commercial data analysis tools.
Analysis of injection/production data at associated injectors and producers reveals the optimum injection levels depending on the significant factors including but not limited to subsurface conformance, number and location of producers, vintage of wells, completion practices and injection history. The optimum injection levels change depending on the changing variables that affect the displacement and injection processes, thus, a real-time data flow from producers and injectors is required to capture and maintain the optimum operating levels.
The significance of each parameter in this process is obtained in a dynamic manner with real-time feed of field data and efficiently used to determine the optimum levels of injection at a specified time. Change of important factors in the process in time is also important by means of adding another dimension on the relative significance of parameters in the process, thereby shedding light on future decisions.
Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Ranjith, Rahul (University of Southern California) | Tuna, Tayfun (University of Houston) | Nabizadeh, Mehdi (International Petro Asmari Company) | Kadkhodaei, Nematollah (International Petro Asmari Company) | Temizel, Cenk (Area Energy)
There are certain online tools that serve as a comprehensive toolbox in specific areas of engineering including but not limited to chemical and mechanical engineering. These tools provide quick online access to a broad range of equations used in the area of interest while serving as a convenient tool for professionals that do not have access to a comprehensive library or that are not familiar enough with the subject to locate the equation required. Thus, the objective of online Petroleum Engineering Toolbox is to provide users in academia and the industry - with or without petroleum engineering background - a comprehensive and convenient 24/7 accessible source for petroleum engineering and related calculations, offering calculations and technical description of over 1000 formulas. Petroleum Engineering Toolbox consists of 2 main sections: (1) Equations, (2) Technical Manual / Reference featuring a total of over thousand calculations in Reservoir, Drilling, Production, Well Testing, Flow, Laboratory Experiments, Economics, PVT, Logging, Optimization, Well Stimulation, EOR and Thermodynamics. The Technical Manual/Reference section is to serve as a library for reference tables, charts, tables in petroleum engineering, thus, providing a very convenient tool for engineers working anywhere in the world where it is hard to access sources of information including fields, offshore and onshore remote locations. It outlines the theory of equations used in calculations with units for the most convenient and user-friendly experience. The Petroleum Engineering Toolbox is available online and as a mobile application for better use on mobile devices. Its online interface is entirely built on top of open source technology. Server side connection is done by Apache 2.4.9
Temizel, Cenk (Aera Energy) | Saputelli, Luigi (Frontender Corp.) | Nabizadeh, Mehdi (International Petro Asmari Company) | Balaji, Karthik (University of Southern California) | Suhag, Anuj (University of Southern California) | Ranjith, Rahul (University of Southern California) | Wijaya, Zein (HESS)
In field development and management, optimization has turned out to be an integral component for decision-making. Optimization involves computationally intensive complex formulations but simplifies making decisions. For reaching the optimal solution to a defined objective function, optimization software can be combined with a numerical reservoir simulator. Hence, robust and faster results are imperative to optimization problems.
To maximize cumulative recovery and net present value (NPV), the reservoir simulator works on maximizing these predefined objective functions that can be multi-objective leading to Pareto sets with "trade-offs" between objectives. In optimization algorithms with predefined objective functions, there is a need for these objective functions to be flexible by using conditional statements through procedures, since generally they do not provide the flexibility required by the physical reservoir fluid flow phenomenon to "maneuver" throughout optimization iterations.
In this study, a commercial reservoir simulator is coupled with an optimization software. As the need was discuss earlier, conditional statements are implemented in the simulator as procedures. Operating the software/simulator combination under pseudo-dynamic objective functions is achieved through these procedures. Highest recovery for the time period mentioned in the conditional statement for the simulation is achieved by trying sets of combinations of parameters, which also makes the optimization process faster and more robust. Throught the use of these conditional statements, the procedures are able to implement piecewise objective functions as codes for a given time frame.
The objective function to be maximized by the optimization process in this study cumulative production. The optimized recoveries with pseudo-dynamic objective functions provide an enhanced recovery, as compared to that of an optimization case with predefined constant objective function in the optimization software throughout the iterations of the optimization and simulation process.