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Electrical resistance heating provides key advantages over other thermal recovery methods in the recovery of heavy oil resources. These advantages include low upfront capital expenses, more control on the delivery of the heat spatially, easiness of permitting in environmentally sensitive areas as well as environmental and economic benefits due to lower carbon footprint. However, the recovery efficiency is relatively lower compared to more conventional methods such as CSS, steamflood and SAGD processes as it doesn't introduce a (pressure) drive mechanism and radius of impact is relatively small which may result in marginal economics.
A comprehensive review of the technology with all the technical and economic details on the deployment of the electrical resistance heater is provided. A full-physics commercial reservoir simulator is utilized to model a benchmark model and it is coupled with a robust optimization and uncertainty tool to investigate the significance of the control and uncertainty variables in the system. Propagation of the heat, increased the radius of impact, production performance, energy input and economics are outlined in comparison to the base case where the horizontal well is modeled without the extra laterals. Production engineering and deployment aspects are all provided in detail, as well.
Utilization of electrical resistance heaters on multilateral wells provides improved economics due to the increased recovery with the additional accessible reservoir volume for heating with the reduced cost of the additional laterals as opposed to the major cost of the main wellbore. The improved unit cost for the heater per foot also helps the economics, thus increased the radius of impact translates into better recovery at lower unit costs. Model inputs as well as the results including the production performances, significance of key parameters and economics, are outlined in a comparative manner.
Electrical resistance heating is not a new process but has recently gained more attention due to the advances in the materials used providing better durability, however, the recovery process needs special designs that bring down the unit cost to make the projects feasible. This study provides a new approach in improving recovery in electrical resistance heating methods that may help to turn several potential marginal projects into projects with more favorable economics in a method which has a great potential in an industry becoming more environmentally sensitive.
Latest technological developments and applications made optimal control methods usage in optimal well placement in intelligent fields practical and beneficial to increase the production. Effective usage of these methods strongly depends on the detailed evaluation of the economic view and performance in reservoirs that have high uncertainty, particularly. There are several methods of optimization of well placement ranging from classical reservoir engineering to derivative-free and hybrid methods.
TNO's Olympus model used globally as a benchmark model in ISAAP-2 Challenge in used. Geological modeling software is coupled with the commercial full-physics reservoir simulator as well as the optimization software in order to produce different geological realizations to represent the geological uncertainty and run the simulation model with differing inputs of optimization and uncertainty in a loop. Results are outlined in detail in a comparative way including comparison to the previous study to illustrate the challenges and benefits of smart wells and optimization of placement of them in intelligent fields.
Results indicate that classical reservoir engineering principles still prove useful in the beginning of the optimization process. Then, derivative-free and hybrid methods introduce significant improvement on economics. There are certain challenges in CPU requirements however the state-of-the-art facilities provided significant reduction in runtimes along with the help of the hybrid methods where proxies are built and used for faster runtimes. Despite higher initial capital expenses, smart wells provide significant advantages in recovery and economics compared to that of the conventional wells where these is less control on the production/injection at the layer level. Literature lacks a comprehensive study that takes into account the optimization of well placement in smart fields focusing on smart wells and the all major available methods for optimization. This study closes that gap providing a strong reference building on top of the previous study extending it to intelligent fields which are becoming very common and useful in oil and gas industry in conventional and unconventional applications.
Micellar – Polymer drive process is one of the effective, proven and widely used non-thermal EOR methods which classified under chemical flooding. The process is preferable in reservoirs that don’t have enough aquifer assistance and also in depleted reservoirs. A successful micellar-polymer flooding operation can be enabled by having correct data of parameters like reservoir pressure, mineral types in the reservoir, phase behavior of microemulsions, reservoir temperature, salinity data, buffer stability, micellar slug, and concentrations of the surfactants.
In this study, A comprehensive literature review regarding on above parameters studied with field case studies worldwide. A Micellar-polymer drive process is applied on a stochastic reservoir and the optimization of the case performed by considering the mechanisms and limitations of micellar-polymer drive process, selection and design criteria, as well as the phase behavior changes during the process to have the most effective residual oil recovery. Parameters that enables an optimal recovery is described and used as optimization parameters in a full-physics commercial reservoir simulator.
Typical Injection sequence that includes water flooding, polymer injection, polymer drive, polymer taper and chase water is applied for selected time periods. Changes of Oil saturation, water viscosity, adsorbed fluid, surfactant and polymer adsorption is simulated by using the optimal values of selected optimization parameters. General solution results are given with the optimal solution and all compared with the base case. It clarified that the Micellar-polymer drive optimization maximizes cumulative oil recovery in a reservoir that has a stochastically generated permeability distribution.
DTS/DAS applications provide key advantages in surveillance and better understanding of both unconventional and thermal operations in terms of key attributes including but not limited to conformance, wellbore integrity in better spatial and temporal terms. This study investigates the effects of CO2 and Naptha in enhancing the steamflood process while incremental benefits are achieved through improved monitoring of the steamflood injection process using DTS/DAS applications.
A full-physics simulator is used to model the process. The technical as well as economic details of deployment of DTS/DAS as well as the steam-additive process are outlined in detail. Sensitivity study carried out on the model indicates the key attributes along with their significance. Athabasca bitumen properties are used. CO2 additive increases the steam chamber size but lowers the steam temperature while naptha/CO2 additives lower the viscosity, thus optimization study carried out the optimum operating levels of the additives not only in physical production/injection terms but also in terms of economics.
The results indicate better reservoir management with DTS/DAS applications compared to the base case and injection can be monitored and adjusted better with such tools. The objective function built with economic parameters helped to maximize the NPV for the project, providing a more realistic perspective on the projects. DTS/DAS applications prove useful not only in terms of production performance but also in terms of economics. Physical properties of CO2 and naptha indicate that the two have different dominant modes of improving recovery of steam only injection. CO2 increases the extent of the steam chamber while lowering the steam temperature significantly.
This study approaches the delicate process of additive use in steam processes while coupling the additional benefits of use of DTS/DAS applications in optimizing the recovery and the economics outlining the key attributes and the challenges and best practices in operations serving as a thorough reference for future applications.
Capacitance/resistance modeling (CRM) is an empirical waterflood modeling technique based on the signal correlations between injection rates and gross production rates. CRM can satisfactorily estimate the gross (liquid) production rate. The oil-production-rate forecast is based on fitting the empirical oil fractional-flow model, the Leverett (1941) oil fractional-flow model, or the Koval (1963) model to the historical production data. We observed that the oil-production-rate forecast in this approach is less satisfactory.
We propose a robust approach that combines CRM gross production prediction with a Buckley-Leverett displacement-theory-based waterflood analytical method—the Y-function method—to calculate the oil fraction flow and to improve the oil prediction capability. The analytical method is based on the results of the historical production performance of either an individual producer or a group of producers in a given area. By using this method, a better understanding can be developed about the production performance, such as the breakthrough time of injected water and possible operational issues, such as water channeling. The analytical model compares oil fractional flow and the cumulative gross production on the producers, yet the value of saturation is not required. As a result, the forecast of the oil-production rate becomes more convenient and straightforward.
Sayarpour et al. (2009a) outlined field examples to compare the estimated oil production obtained using the current empirical oil fractional flow-model approach and the analytical Y-function method. The new method provided another effective way to calculate the oil rate in CRM. The results indicated that the new approach improved the accuracy of the oil-rate calculation and proved convenient in field applications. The objective of this study was not to regenerate the gross-rate forecast of CRM, but rather to improve the oil fractional-flow description and oil-production-rate forecast from the gross rate using the Y-function method.
Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Ege University) | Palabiyik, Yildiray (Istanbul Technical University) | Irani, Mazda (University of Calgary / Ashaw Energy) | Balaji, Karthik (University of North Dakota) | Ranjith, Rahul (Far Technologies)
Steam-assisted Gravity Drainage (SAGD) is one of the major thermal recovery methods for heavy oil. Optimization of SAGD is a delicate process that needs to be planned and operated in a detailed manner. Steam trapping is one of the methods that may help optimize production in SAGD by keeping the steam chamber well drained, where liquid does not accumulate on top of the producer and steam is not produced. This is a challenging process even with the advances in well completions with smart or intelligent wells. In this study, the use of smart valves (ICVs) are investigated and their use in optimization of SAGD through steam trapping is outlined.
A comprehensive review on steam trapping, in terms of theory and practice, has been provided. A smart well configuration with intelligent valves are built in a representative reservoir simulation model where the full-physics commercial reservoir simulator is coupled with an optimization/sensitivity software to optimize the processes and investigate the significance of the key control/decision and uncertainty variables. Different approaches are used in steam trap control; static location, dynamic scanning in time and location, and dynamic scanning in time and specified locations. The results are outlined along with practical aspects of the whole process and operation.
The results are outlined in a comparative way to illustrate the benefits of smart valves and the key points in utilizing them, including economic aspects of their use for additional recovery and the related costs. Results indicate that intelligent wells may prove useful in optimizing steam trapping in SAGD operations depending on the size of the prize.
There are several studies on steam trapping. However, there aren't many studies that integrate steam trap control with smart wells. This study investigates the theoretical and practical aspects of steam trapping using intelligent wells, along with outlining the key attributes, decision and uncertainty variables in a comparative way in terms of the steam trap control strategies and economics.
Introducing heat into a heavy oil reservoir is a well-known enhanced oil recovery (EOR) technique. Cyclic steam injection (CSI), also called as huff and puff, is one of the most common methods used to heat the reservoir to reduce fluid viscosity. Natural fractures provide effective connections throughout the reservoir, which make naturally fractured reservoirs great candidates for steam injection. Numerical simulators are capable of designing CSI operations in dual-porosity systems. However, the use of commercial simulator can be time-consuming when a large number of cases are examined for optimizations. Artificial neural networks (ANNs) work well to solve and classify the non-linear relationships between input and output parameters.
In this paper, one forward and two inverse ANN models are proposed to discuss the performance of CSI in naturally fractured heavy oil reservoirs and to discuss smart proxy models’ mimicking ability of the commonly used numerical models. In this study, we focus on a single injection and soaking cycle for a single well. The first stage of the production starts with initial recovery, and it continues until the oil flow rate drops down to a predetermined threshold value, then, the second stage (the injection phase) takes place. Injection phase is followed by the soaking phase. Delivery of steam creates a heat chamber around the wellbore (stimulated zone) where the reservoir temperature dramatically increases. The non-uniform temperature distribution of the reservoir ends up encountering different viscosity values of the oil.
The forward ANN model successfully forecasts both cumulative oil production profiles and viscosity changes around the wellbore. Reservoir properties, rock and fluid interaction parameters, fluid properties, and injection design parameters (such as steam quality, injection rate, injection time, etc.) were used as input features. A variety of ANN architectures were tested for the minimum testing errors.
The first inverse-looking ANN model, Inverse Model 1, was designed for determining the ideal injection design parameters for a desired cumulative production profile. Once injection design parameters were selected, the forward ANN model was run for those injection design parameters for verification purposes.
Second inverse-looking ANN model, Inverse Model 2, characterizes significant reservoir parameters including; matrix porosity, matrix permeability, fracture permeability, and fracture spacing. It can be laboriously challenging and computationally costly to obtain these parameters for some fields. This ANN model is trained with injection design parameters and resulting performance indicators. All the ANN-based models are controlled by a user-friendly graphical interface for the ease of the user.
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 the O&G 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 take 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 data-driven methods illustrate the advantages and disadvantages of the methods and 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 the use of data-driven method for natural gas injection in shale oil, a comparative approach including the physics-based methods is included but they 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.
Temizel, Cenk (Aera Energy) | Irani, Mazda (Ashaw Energy) | Canbaz, Celal Hakan (Schlumberger) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (Smart Recovery) | Balikcioglu, Aysegul (USC) | Diaz, Jose M. (VCG O&G Consultants) | Zhang, Guodong (China Petroleum Eng and Construction Corp.) | Wang, Jie (College of Technological Studies) | Alkouh, Ahmad
As major oil and gas companies have been investing in renewable energy, solar energy has been part of the oil and gas industry in the last decade. Originally, solar energy was seen as a competing form of energy source as a threat that may replace or decrease the share of fossil fuels as an alternative energy resource in the world. However, oil and gas industry has adapted to the wind of change and has started investing and utilizing the solar energy significantly. In this perspective, this study investigates and outlines the latest advances, technologies, potential of solar both as an alternative and a complementary source of energy in the Middle East in the current supply and demand dynamics of oil and gas resources.
A comprehensive literature review focusing on the recent developments and findings in the solar technology along with the availability and locations are outlined and discussed under the current dynamics of the oil and gas market and resources. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research to non-technical but renowned resources including journals and other publications including raw data as well as forecasts and opinions of respected experts. The raw data and expert opinions are organized, summarized and outlined in a temporal way within its category for the respective energy source.
Solar energy is discussed from a perspective of their roles either as a competing or a complementary source to oil and gas. In this sense, this study goes beyond only providing raw data or facts about the energy resources but also a thorough publication that provides the oil and gas industry professional with a clear image of the past, present and the expected near future of the oil and gas industry as it stands with respect to renewable energy resources.
Among the few existing studies that shed light on the current status of the oil and gas industry facing the development of the renewable energy are up-to-date and the existing studies within SPE domain focus on facts only lacking the interrelationship between solar energy and oil and gas such as solar energy used in oil and gas fields as a complementary green energy.