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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.
Clemens, Carter (BP) | Rivas, Bruno S. (Mexico National Hydrocarbons Commission) | Atkinson, Angela Dang (Encana Corp.) | Mohan, Jesma (Schlumberger) | Garg, Lavish (Weatherford) | Pradhan, Yogashri (Endeavor Energy Resources) | Ighalo, Samuel (Halliburton) | Nunoo, Nii Ahele (NOV) | Mandzhieva, Radmila (Independent) | Lal, Tarang (Aera Energy)
Special Section: The Value and Future of Petroleum Engineering
JPT asked several active young professionals about their career path thus far and what they liked about petroleum engineering. Here are some of their answers.
Carter Clemens, BP
I lucked into the petroleum industry; I did not know much about it before choosing it as a major at the University of Texas. It has allowed me to live and travel to distant countries I never thought I would visit—whether it is Abu Dhabi, Port of Spain, Cairo, or Aberdeen, the oil industry has an incredible reach to some interesting locations. It has also enabled me to pursue engineering while spending a lot of my time outside instead of in front of a computer screen. When I was riding around with well operators in Wyoming and Colorado, I thought of how lucky I was to not be in a cubicle. There is something special about being on a well-site surrounded by snow in Wyoming or watching a sunrise from a rig in the middle of the ocean—you can’t get that with most industries.
Bruno S. Rivas, Mexico National Hydrocarbons Commission
Petroleum engineering is more than get-ting oil out of the ground; it means delivering the energy that the world needs to fight poverty, increase human wellness, and accelerate growth in a sustainable way. The oil and gas industry has given me the opportunity to interact with professionals from all over the world, to exchange different experiences, to solve problems in a responsible and efficient manner, and to inspire future generations. With no doubt, if I had to decide again what to study, my choice would be oil and gas; it is certainly not an easy path, but realizing that I’m generating a positive impact on others’ lives is a personal satisfaction.
Let’s Talk Climate Change
Angela Dang Atkinson, Encana Corp.
I love saying, “I’m a petroleum engineer and I believe in anthropogenic climate change.” It catches people off guard and begins a nuanced conversation about energy. It is an opportunity for me to talk about the importance of incremental change and that there is no silver bullet in solving the world’s energy challenges. As Harvard economics professor Ed Glaeser states, “Once we start thinking that there’s a silver bullet…we lose the fact that we need to be working day by day, over decades, to effect change.” We, the oil industry, are among those working day by day to effect change—whether we are increasing the use of recycled fracture water or finding creative ways to reduce emissions, these are the types of incremental gains on the way to better energy solutions. This nuanced conversation should not primarily exist in 150-character tidbits online. It is up to us to have that conversation in a grassroots manner, face to face, with our community.
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.
Temizel, Cenk (Aera Energy) | Canbaz, Celal Hakan (Schlumberger) | Tran, Minh (USC) | Abdelfatah, Elsayed (University of Calgary) | Jia, Bao (University of Kansas) | Putra, Dike (Rafflesia Energy) | Irani, Mazda (Ashaw Energy) | Alkouh, Ahmad (College of Technological Studies)
Petroleum in general is found in sub-surface reservoir formation amongst pores existent in the formation. For several years due to lack of information regarding production and technology, free-flowing, low viscosity oil has been produced known as conventional crude oil. Fortunately, in recent times, due to advancement of technology, high viscosity with higher Sulphur content-based crude has been produced known as heavy oil. There are also exists significant difference in volatile materials as well as processing techniques used for the two types of crude. (
Heavy Oil can be used by definition internationally to describe oil with high viscosity (Although the Oxford dictionary might have several variations of the same, within the contents of this paper, we refer to heavy oil as high viscosity crude). Heavy oil generally contains a lower proportion of volatile constituents and larger proportion of high molecular weight constituents as compared to conventional crude oil (often referred to as light oil, we shall describe the characteristics of the types of oil further in the introduction). The heavy oil just doesn't contain a composition of paraffins and asphaltenes but also contains higher traces of wax and resins in its composition. These components have larger molecular structures leading to high melting and pour points. This makes the oil a bad candidate for flow profiles and adversely affects the mobility of the crude. ( Recovery: Low viscosity and high melting points Processing: Higher Resin, Sulphur and aromatic content Transportation: Low Viscosity
Recovery: Low viscosity and high melting points
Processing: Higher Resin, Sulphur and aromatic content
Transportation: Low Viscosity
These all together impact the economics related to E&P (Exploration and Production) of heavy oil resources. These resources generally have a higher of production associated with them and are one of the first candidates to be affected by reduction of crude prices as seen in 2014 and early 2015. Crude oil can generally be classified into its types by using its API values that are generally obtained through lab testing.
One method of reducing the recognized threat of global warming is using continued sequestration of anthropogenic "greenhouse gases," such as carbon dioxide (CO2). Sedimentary basins are present globally and, because of the omnipresent nature of deep, regional-scale aquifers within them, they can be considered as potential sites for disposal and sequestration of CO2. Successful implementation requires identifying and considering fundamental concepts to help ensure that CO2 is stored in the aquifers effectively. The ideal scenario involves migrating CO2 from injection wells to remote storage sites using the aquifer, helping ensure its isolation from the atmosphere for a considerable length of time. In addition to the scientific and technical aspects of sequestration research, the practicality of the concept should be considered, including evaluating the maximum possible volume of CO2 that can be stored at global and regional levels as well as the safety and economic feasibility of the process. This study discusses examples to help provide an in-depth, practical understanding of this concept.
The study combines a full-physics commercial simulator with an effective uncertainty and optimization tool. The sequestration phenomenon is then modeled to investigate the significance and effect of the essential parameters on well performance while also considering thermal and geochemical effects. The process assesses the injection of CO2 containing tracers for 25 years, followed by shutting in the injectors and modeling the status of CO2 for the next 225 years. While CO2 is injected into an aquifer, the molecular diffusion of CO2 in water is modeled. The modeling of the thermal effects attributable to the injection of CO2 is important because the chemical equilibrium constants have a functional thermal dependency.
For reservoir management, the evaluation and effective management of uncertainties are as important as managing the well-level parameters. For this study, essential reservoir and well parameters are identified, and sensitivity and optimization processes are performed on them; the tornado charts in this paper illustrate the significance and effect of each parameter. Thermal and geochemical effects are shown to play vital roles in the sequestration process.
This study outlines the significance of essential parameters associated with the overall success of the CO2 sequestration in aquifers using in-depth uncertainty and optimization analysis, and it considers the influence of thermal and geochemical effects.