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.
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 (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.
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) | Irani, Mazda (Ashaw Energy) | Canbaz, Celal Hakan (Schlumberger) | Palabiyik, Yildiray (Istanbul Technical University) | Moreno, Raul (CSmart Recovery) | Diaz, Jose M. (VCG O&G Consultants) | Tao, Tao (Texas Southern University) | Alkouh, Ahmad (College of Technological Studies)
Along with the advances in technology, greener technologies that help to minimize carbon footprints are becoming more common in oilfield applications as well as other areas. Electrical heating is one of the relatively more environmentally-friendly heavy oil recovery technologies that is not new but has gained more popularity with the advances in electrical heating equipment and the technologies within the last decade offering longer and reliable operations that led to its use as a standalone recovery method rather than only a preheating method. In this study, a comprehensive investigation of the production optimization is outlined that includes not only the reservoir aspects but also the production and facility aspects of electrical heating in heavy oil reservoirs. A full-physics commercial simulator has been coupled with an optimization/uncertainty tool to understand the significance of uncertainty and control variables that influence the production function in addition to the analysis of normalized type curves in different real field cases. The challenges encountered during implementation of electrical heating processes in terms of production, reservoir and facilities engineering are outlined in order to provide a comprehensive and practical implementation perspective rather than only theoretical and/or simulation work. It is observed that electrical heating can be promising when applied in the right place and can bring lots of benefits not only in terms of low water-cut recovery, but also low carbon footprint and low costs associated with environmental fees. The significant parameters are listed for a robust and successful implementation of an electrical heating project. There are studies on electrical heating, but they are either outdated reflecting the old technology, or only focusing on simulation/theoretical work or only case focusing only reservoir or production aspects. This study fills the gap and provides a comprehensive look in detail in the theory, real-field practical problems and solutions from source of electricity to production of the heavy oil illustrating the costs associated that can serve as a solid reference for future implementations. 2 SPE-193707-MS
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.
Salehian, Mohammad (Istanbul Technical University) | Temizel, Cenk (Aera Energy LLC-EBS) | Gok, Ihsan Murat (Istanbul Technical University) | Cinar, Murat (Istanbul Technical University) | Alklih, Mohammad Y. (ADNOC)
Use of smart well technologies to improve the recovery has caught significant attention in the oil industry in the last decade. Capacitance-Resistance (CRM) methodology is a robust data-driven technique for reservoir surveillance. Reservoir sweep is a crucial part of efficient recovery, especially where significant investment is done by means of installation of smart wells that feature inflow control valves (ICVs) that are remotely controllable. However, as it is a relatively newer concept, effective use of this new technology has been a challenge. In this study, the objective is to present the efficient use of ICVs in intelligent fields through the integrated use of capacitance-resistance modeling and smart wells with ICVs.
A standard realistic SPE reservoir simulation model of a waterflooding process is used in this study where the smart well ICVs are controlled with conditional statements called procedures in a fully commercial full-physics numerical reservoir simulator. The simulation data is utilized to build the CRM model to obtain the inter-well connectivities at the zonal level beyond only the inter-well connectivity data as smart wells provide control and information on the amount of injection into each layer or zone. Thus, after analyzing the CRM model to detect the inter-well connectivities at the zone/layer-level in an iterative way, the optimum injection not only at the well level but also at the perf/zone level is found. The workflow is outlined as well as the improvements in the results.
The smart well technology has been challenged with the associated cost component thus, it is important to present the benefits of this technology with applications in more diverse cases with different workflows. It has been observed that a robust reservoir characterization in an intelligent field can provide an insight into the physics of reservoir including smart wells with ICVs. The results are presented in a comparative way against the base case to illustrate the incremental value of the use of ICVs along with key performance indicators. Most importantly, it has been shown that smart well use 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 see the big picture at the field level to improve the performance of the reservoirs beyond individual well performances taking into account the interference between wells. This method takes the reservoir surveillance to the next level where reservoir characterization is improved using smart field technologies and capacitance-resistance modeling as a robust cost-effective data-driven method.
Temizel, Cenk (Aera Energy) | Zhiyenkulov, Murat (Schlumberger) | Ussenova, Kamshat (Schlumberger) | Kazhym, Tilek (Embamunaygas) | Canbaz, Celal Hakan (Schlumberger) | Saputelli, Luigi Alfonso (Frontender Corporation)
Optimum well placement in intelligent fields, using previously developed optimal control methods to maximize net present value (NPV), is becoming practical with recent advances in technologies as well as their applications to the petroleum industry. To efficiently use these methods in an intelligent field, an assessment of its economic aspects and its performance, especially in reservoirs with high degree of heterogeneity (uncertainty), must be made. By using such integrated workflows, mature and new field can be developed better. The workflow could be used as a reliable tool for improving the decision-making process.
There are multiple optimization techniques used in the industry for optimizing well placement (e.g. direct and gradient optimization). With the use of reservoir simulation case study, this paper aims to provide a comparative performance analysis of multiple optimization techniques. To make the evaluation stronger and more application to a real-world problem, the model selected for this study has a high degree of geological uncertainty and constraints for computation time, infrastructure and complexity to decide on optimal well placements.
Having a better understanding on the uncertainties in geology lead to more robust decisions in reservoir management. Right strategy especially helps in optimizing larger scale, million-cell model simulations enabling practical implementation of reservoir simulation coupled with optimization.
Optimum well placement in complex reservoirs requires a complete grasp of optimization methods, key factors and constraints but most importantly the effect of geological uncertainty. A lack of awareness of optimization algorithms and their applications by engineers is a drawback in this process. In addition, complete evaluation of geological uncertainty is another challenge. This study provides an understanding and clarification to serve as a guideline on optimization practices by outlining the significant components in the process.
Balaji, Karthik (University of North Dakota) | Rabiei, Minou (University of North Dakota) | Canbaz, Hakan (Schlumberger) | Agharzeyva, Zinyat (Texas A & M University) | Tek, Suleyman (University of the Incarnate Word) | Bulut, Ummugul (Texas A&M University-San Antonio) | Temizel, Cenk (Aera Energy LLC)
Data-driven methods serve as a robust tool to turn data into knowledge. Historical data generally has not been used in an effective way in analyzing processes due to lack of a well-organized data, where there is a huge potential of turning terabytes of data into knowledge. With the advances and implementation of data-driven methods data-driven models have become more widely-used in analysis, predictive modeling, control and optimization of several processes. Yet, the industry overall is still skeptical on the use of datadriven methods, since they are data-based solution rather than traditional physics-based solutions; even though physics and geology are sometimes part of this methodology. This study comprehensively evaluates the status of data-driven methods in oil and gas industry along with the recent advances and applications.
This study outlines the development of these methods from the fundamentals, theory and applications of these methods along with their historical acceptance and use in the industry. Major challenges in the process of implementation of these methods are reviewed for different areas of applications. The major advantages and drawbacks of data-driven methods over the traditional methods are discussed. Limitations and areas of opportunities are outlined. Latest advances along with latest applications and the associated results and value of the methods are provided.
It is observed that the successful use of data-driven methods requires strong understanding of petroleum engineering processes and the physics-based conventional methods together with a good grasp of traditional statistics, data mining, artificial intelligence and machine learning. Data-driven methods start with a data-based approach to identify the issues and their solutions. Even though data-driven methods provide great solutions on some challenging and complex processes, that are new and/or hard to define with existing conventional methods, there is still skepticism in the industry on the use of these methods. This is strongly tied to the delicacy and sensitive nature of the processes and on the usage of the data. Organization and refinement of the data turn out to be important components of an efficient data-driven process.
Data-driven methods offer great advantages in the industry over that of conventional methods under certain conditions. However, the image of these methods for most of the industry professionals is still fuzzy. This study serves to bridge the gap between successful implementation and more widely use and acceptance of data-driven methods, and the fuzziness and reservations on the understanding of these methods in the industry. Significant components of these methods along with clarification of definitions, theory, application and concerns are also outlined in this study.