Temizel, Cenk (Aera Energy) | Balaji, Karthik (University of North Dakota) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Rabiei, Minou (University of North Dakota) | Zhou, Zifu (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Due to complex characteristics of shale reservoirs, data-driven techniques offer fast and practical solutions in optimization and better management of shale assets. Developments in data-driven techniques enable robust analysis of not only the primary depletion mechanisms, but also the enhanced oil recovery in unconventionals such as natural gas injection. This study provides a comprehensive background on application of data-driven methods in oil and gas industry, the process, methodology and learnings along with examples of data-driven analysis of natural gas injection in shale oil reservoirs through the use of publicly-available data.
Data is obtained and organized. Patterns in production data are analyzed using data-driven methods to understand key parameters in the recovery process as well as the optimum operational strategies to improve recovery. The complete process is illustrated step-by-step for clarity and to serve as a practical guide for readers. This study also provides information on what other alternative physics-based evaluation methods will be able to offer in the current conditions of data availability and the understanding of physics of recovery in shale oil assets together with the comparison of outcomes of those methods with respect to the data-driven methods. Thereby, a thorough comparison of physics-based and data-driven methods, their advantages, drawbacks and challenges are provided.
It has been observed that data organization and filtering takes significant time before application of the actual data-driven method, yet data-driven methods serve as a practical solution in fields that are mature enough to bear data for analysis as long as the methodology is carefully applied. The advantages, challenges and associated risks of using data-driven methods are also included. The results of comparison between physics-based methods and data-driven methods illustrate the advantages and disadvantages of each method while providing the differences in evaluation and outcome along with a guideline for when to use what kind of strategy and evaluation in an asset.
A comprehensive understanding of the interactions between key components of the formation and the way various elements of an EOR process impact these interactions, is of paramount importance. Among the few existing studies on natural gas injection in shale oil with the use of data-driven methods in oil and gas industry include a comparative approach including the physics-based methods but lack the interrelationship between physics-based and data-driven methods as a complementary and a competitor within the era of rise of unconventionals. This study closes the gap and serves as an up-to-date reference for industry professionals.
Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Putra, Dike (Rafflesia Energy) | Asena, Ahmet (Turkish Petroleum Corp.) | Ranjith, Rahul (Far Technologies) | Jongkittinarukorn, Kittiphong (Chulalongkorn University)
Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way.
History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field.
Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases.
Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.
The objective of this work is to evaluate the efficacy of empirical models in forecasting oil production in shale reservoirs, bycomparing and analyzing their fit and effectiveness to our dataset. The following three modelswere considered: A Conventional Decline Curve Analysis (CDC), an Unconventional Rate Decline (URD) Approach, and a Logistics Growth Analysis (LGA) method. A comparative study is performed to evaluate the use of Artificial Neural Networks (ANN) for production forecasts and to reinforce the thinking that it is imperative to include physical parameters in mathematical models to predict accurateforecasts. For this project, we used nonlinear regression to fit empirical models to the dataset obtained from North Dakota Industrial Commission (NDIC). We evaluated the fit of modelswith the help of coefficient of determination. Physical parameters, such as porosity, saturation, shale volume, etc., and log data from sonic logs, gamma ray logs, etc., were selected as input to the ANN model andwere aided by Analysis of Variances (ANOVA). Amongst the empirical models for shale play, URD method is the most commonly used since it is idealfor fractured reservoirs with extremely low permeability. URD model did fit the cumulative production profiles, but could not accurately fit the monthly production profile. The CRD approach was overallunsuccessful in generating accurate future production profiles.
Temizel, Cenk (Aera Energy) | Ranjith, Rahul (University of Southern California) | Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Dhannoon, Diyar (Texas A&M University) | Saracoglu, Onder (Consultant)
Disposal and long-term sequestration of anthropogenic "greenhouse gases", such as, CO2 is a proposed approach for reducing global warming. Deep, regional-scale aquifers in sedimentary basins are possible sites for sequestration, given their ubiquitous nature. For Carbon dioxide (CO2) to be stored in aquifers responsibly, it is essential to identify key concepts that need to be considered for potential implementation. Ideally, the injected CO2 will migrate through an aquifer from injection wells to remote storage sites, and remain isolated from the atmosphere for a considerable period of time. Fundamental topics of interest in sequestration research are not just concerned with scientific and technical aspects, but also with practical concerns, such as, economic feasibility of storage, safety, and the maximum possible amount of CO2 storage globally and for specified regions. Thus, it is crucial to have a robust understanding of this important process not only in theory, but in practice as well through illustration with solid examples as in this study.
A robust commercial optimization and uncertainty software is coupled with a full-physics commercial simulator that models the phenomenon to investigate the significance of major parameters on performance of wells in CO2 sequestration, under geochemistry and thermal effects. CO2 injection is first done for 25 years then the injector is shut-in and the molecular diffusion of CO2 in water is modelled for the next 225 years. Thermal effects due to injection of CO2 are also modelled.
Sensitivity and optimization have been done on major reservoir parameters, such as, fluid and rock properties and well operational parameters, and then significance of each has been illustrated through tornado diagrams. It is observed that a robust approach on handling of uncertainties in the reservoir is as important as management of well operational parameters in the scope of reservoir management. Presence of impact of geochemistry and temperature effects have proven to play an important role in the process.
This study provides an in-depth optimization and uncertainty analysis to outline the significance of each major parameter involved in the performance, and CO2 sequestration in aquifers where influence of temperature and geochemistry is present.
Temizel, Cenk (Aera Energy) | Ranjith, Rahul (University of Southern California) | Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Putra, Dike (Rafflesia Energy) | Saracoglu, Onder (Consultant)
Integrated asset modeling (IAM) offers the oil industry several benefits. Next-generation reservoir simulators help achieve faster runtimes, insight into interaction between various components of a development, can be used as an effective tool in detecting bottlenecks in a production system, and as a constant and more effective communication tool between various departments. IAM provides significant opportunities for optimization of very large or complex infrastructures, and life-of-field analysis of production optimization scenarios.
Simultaneous modeling of surface and subsurface components helps reduce time and enhances efficiency during the decision-making process, which eliminates the requirement for tedious, time-consuming work and iterations between separate solutions of reservoir and surface networks. Beyond this convenience, the technology makes it possible to reach more robust results quicker using surface-subsurface coupling. The objective of this study is to outline the advantages and challenges in using next-generation simulators on simulation of multiple reservoirs in integrated asset management.
Simultaneous simulation of multiple reservoirs adds another dimension of complexity to the process of IAM. Several sub-reservoir models can be simulated simultaneously in large fields comprising sub-reservoirs with complex surface systems, which could otherwise become very tedious to handle. In this study, a next-generation reservoir simulator is coupled with an optimization and uncertainty tool that is used to optimize net present value of the entire asset. Several constraints and bottlenecks in such a large system exist, all connected to one another. IAM proves useful in debottlenecking to increase efficiency of the thorough system. The strengths and difficulties associated with simultaneous simulation and optimization of multiple reservoirs are compared to the conventional way of simulating assets separately, thus illustrating the benefits of using next-generation reservoir simulators during optimization of multiple reservoirs.
The results show that simultaneous solution of the surface-subsurface coupling gives significantly faster results than a system that consists of separate solutions of surface and subsurface. This difference in speed becomes more significant when the number of reservoirs simulated is greater than one. This study outlines the workflow in setting up the model, CPU time for each component of simulation, and the explanation of each important item in this process, to illustrate the incremental benefits of use of next-generation reservoir simulators in simulating multiple reservoirs with surface facilities taken into account.
Although the use of next-generation simulators is becoming more common, solid examples that illustrate the benefits of simultaneous simulation of multiple reservoirs with surface facilities under several different constraints like this study, are important to prove the use of such tools where it is convenient to carry out the optimization in a system that handles decision parameters and constraints simultaneously.
Yegin, Cengiz (Texas A&M University) | Zhang, Ming (Biopharm Frontida) | Suhag, Anuj (University of Southern California) | Ranjith, Rahul (University of Southern California) | Balaji, Karthik (University of Southern California) | Peksaglam, Zumra (University of Southern California) | Dhannoon, Diyar (Texas A&M University) | Putra, Dike (Rafflesia Energy) | Wijaya, Zein (HESS) | Saracoglu, Onder (Consultant) | Temizel, Cenk (Aera Energy)
Current analyses indicate that 50% of oil produced in USA and the world will be through EOR technologies in the next 20-25 years, and heuristics suggest that polymer flooding should be applied in reservoirs with oil viscosities between 10 and 150 mPa.s. The key factor limiting the recommended range is that for oil viscosities greater than 150 mPa.s, where injected water viscosity values required for a favorable mobility ratio give rise to prohibitively low values of polymer injectivity and pumping efficiencies. Herein, we propose that a novel type of supramolecular system based on the complexation of long chain amino amides and maleic acid with reversibly adjustable viscosities can enable us to overcome the injectivity limitation.
The concept is that viscosity of the injected supramolecular system will be maintained initially at low values for easy injection and pumping, and then increased by means of an external pH stimulus just before or upon contacting oil. Our promising lab-scale preliminary studies have indicated that such supramolecular systems possess not only reversible pH-responsive properties, but are also very tolerant to high salinities and temperatures.
While polymers degrade and break up upon experiencing sudden extreme shear stresses and temperatures, supramolecular solutions merely disassemble and re-assemble. Therefore, supramolecular solutions can be considered as healable polymer solutions in a way. Supramolecular solutions can adapt to the confining environment. For instance, when a high molecular weight polymer macromolecule is forced to flow into narrow channels and pores, molecular scission processes may take place.
Supramolecular solutions can have a significant impact in the cases where thermal methods cannot be used for some viscous oils due to thin zones, permafrost conditions and environmental constraints. This project is primarily aimed at developing novel supramolecular assemblies with adjustable viscosity and interfacial properties that have robust tolerance against high temperatures and salinities. Such supramolecular assemblies will be used to significantly improve the feasibility and cost-effectiveness of displacement fluids used in EOR. Overall, there is a significant potential for application of supramolecular solutions in the US and throughout the world.
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.
Ranjith, Rahul (University of Southern California) | Suhag, Anuj (University of Southern California) | Balaji, Karthik (University of Southern California) | Putra, Dike (Rafflesia Energy) | Dhannoon, Diyar (Texas A&M University) | Saracoglu, Onder (Consultant) | Hendroyono, Arief (OXY) | Temizel, Cenk (Aera Energy LLC)
With improvements in technology and increasing amount of opportunities in more challenging assets, the use of smart well technologies to improve recovery has caught significant attention in the oil and gas industry in the last decade. Several workflows have been developed and proposed in order to automate the whole process that integrates several subprocesses focusing on specific parts of the surface or subsurface phenomena. Reservoir sweep is a crucial part of recovery efficiency, especially where significant investment is done by means of installing smart wells that feature inflow control valves (ICVs), which are remotely controllable. However, as it is a relatively newer concept, effective use of this technology has been a challenge. In this study, the objective is to present the efficient use of ICVs in intelligent fields to maximize sweep, and thus, recovery tied to the objective function.
A standard realistic SPE reservoir simulation model of a waterflood process has been used where the smart well ICVs are controlled with conditional statements, called procedures, in a fully-commercial full-physics numerical reservoir simulator. Key performance indicators, including but not limited to water cut and oil rate, are used to adjust the degree of opening of ICVs on the producer side to balance injection on the injector side. This turns out to be a complex phenomenon of higher degree of nonlinearity in a multi-well system in a large field where several wells interact with each other. Objective function seeks to maximize the net present value (NPV) of cumulative oil recovery.
Smart well technologies have been challenged with the associated cost component, thus, it is important to present the benefits of this technology with applications on more diverse cases showcasing different workflows. It has been observed that robust reservoir management in an intelligent field can significantly improve the sweep and recovery by utilization of smart wells with ICVs. The results are presented in a comparative way against the base case to illustrate the incremental value of use of ICVs, along with key performance indicators. Most importantly, it has been shown that the use of smart wells without a robust reservoir management strategy does not always lead to successful results.
In reservoir management, it is not only important to catch the well level details but also to see the big picture at field level for improving reservoir performance beyond individual well performances, taking into account the interference between wells. Although smart wells with ICVs have been deployed on wells all over the field, unfortunately, some similar studies mainly focus on individual or near-wellbore performance rather than the whole asset. In this study, a field-wide approach is followed that integrates data from key performance indicators in an integrated workflow, which outlines efficient integration of variables available to optimize recovery.