bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the Use of a Full-Field Geomechanical Model to Influence and Optimize Field Development Decisions: Case Study from an HPHT Field in South China Sea
Authors: Rai Udai Bhan , Prasetyo Hendro , Tang Hooi Koon , Chua Choon Ling , Fokker Peter , Alfaro Marcela Cid , Kumar Rajan , Adwani Akash , Hansen Kirk S. 
Published: October 2014

Abstract
Drilling overpressured and deep reservoirs is a challenge in itself, but can be complicated by the need to drill through depleted (depressurized) shallower reservoirs. The field under study consists of multiple stacked clastic reservoirs bounded by steeply dipping sealing faults. The deeper reservoirs fall in the high pressure high temperature (HPHT) category and account for one third of the in-place volumes. Ideally, field development for such stacked reservoirs is recommended through the "bottom-up" strategy to prevent late-in-life drilling through depleted zones with reduced drilling window and increased risk of fluid losses and well failure. Here, this would imply drilling and developing the deeper HPHT reservoirs before the shallow, normally pressured reservoirs. From a technical and financial perspective, it is tempting to develop and produce the shallow, normally pressured reservoirs (that contain 70% of the volumes and also have better flow properties) first, and bring the deeper HPHT reservoirs on-stream later. But, is such phased development of the reservoirs possible? Or would producing from the shallower reservoirs first permanently damage our ability to drill and produce the deeper HPHT reservoirs at a later stage in field life? These were the questions we tried to answer in this work.
We built a full-field finite element model to simulate the geomechanical response of the reservoirs to pressure depletion i.e. quantify the displacement, strains and total stress changes in and around the reservoirs as a function of production. Such geomechanical models can serve as a predictive tool to help answer the questions above. In this paper, we show the construction and application of such a geomechanical model in field development planning. Our paper highlights how our geomechanical model results were applied, together with other work, to develop this field safely and efficiently, emphasizing field life cycle value.
Interpretation of Recent SEC Reserves Reporting Guidance
Authors: Morales Enrique , Lee W. John 
Published: October 2014

Abstract
Since the release of its modernized reserves reporting rules in late 2008 (NARA, 2009), the U.S. Securities & Exchange Commission (SEC) has issued little formal guidance on proper interpretation of the modernized rules. The SEC staff issued major guidance in October, 2009, and guidance on a single issue in May, 2013. To supplement the new rules, the Financial Accounting Standards Board has issued revised accounting guidelines (FASB ASC, 2010), which extends some reporting requirements. In addition, we have observed some consistent patterns and clarifications in comment letters which the SEC has made public for filings covering the 2009, 2010, 2011, and 2012 fiscal years. Finally, there have also been a limited number of public presentations by SEC staff (e.g., in November 2009, April 2010, April 2011, August 2013 and June 2014), where SEC staff has provided some clarification on certain positions.
This paper summarizes the more important aspects of the SEC guidance and clarification provided to date, and includes a discussion of issues such as clarification of separate products whose reserves are required to be disclosed, situations in which exemptions to the "five-year rule" are either likely or not likely to be allowed, a refined definition of "project", requirements for third-party reports in conjunction with 10-K or 20-F filings with the commission, status of resources that meet all requirements for proved undeveloped reserves except that they cannot be developed within five years, undeveloped locations more than one offset from existing wells and speed of converting proved undeveloped reserves into developed reserves.
An overview of other key issues raised by the SEC during the last four years is also provided. Potential differences between the SEC regulations and the SPE-PRMS (PRMS, 2007) are described.
Data as an Asset: What The Upstream Oil & Gas Industry Can Learn About "Big Data" from Companies like Social Media
Authors: Perrons Robert K. , Jensen Jesse W. 
Published: October 2014

Abstract
The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but "Big Data"—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.
Service Industry & University Collaboration in Teaching Production Optimization with Artificial Lift
Authors: Chokshi R. N. , Lane W. C. , Dunn-Norman S. , Chumkratoke C. 
Published: October 2014

Abstract
As the number of wells utilizing artificial lift continues to grow globally, educating engineers on all forms of artificial lift and production optimization techniques has become essential. Increasing enrollments in petroleum engineering at universities, combined with decreasing availability of experienced manpower in universities and industry pose significant challenges in offering the most current, relevant, and applicable artificial lift content to students.
This paper details the results of a collaborative effort between the service industry and the academe to develop a semester-long curriculum in artificial lift that has right balance of theory, design and application, and which can ultimately be shared among many petroleum engineering programs. The course has been given successfully at one university during the fall term, 2013. In this offering, students learned from practicing, experienced industry technology leaders using traditional in-person as well as and remote teaching methods. The course encouraged collaborative learning through online sessions and practical problem solving using the current software applications. All lectures were recorded for future use and sharing.
Specific insights from the first course offering, i.e. critical elements which made the collaboration and the course successful, are discussed. The paper also provides information regarding plans to expand the course offering to other universities.
This work is important as it confirms the value of collaboration between industry and the academe to support and enhance petroleum engineering education. The work is unique as it details the mechanisms for improved instruction across multiple petroleum engineering programs. The paper also quantifies both the performance of the students and the student perceptions of the course. This effort is consistent with SPE's many recent initiatives to strengthen petroleum engineering education.
A Novel Methodology for Estimation of Multiphase Flow Properties from Sampling Data of Wireline Formation Tester
Authors: Cig Koksal , Ayan Cosan , Kristensen Morten , Mackay Eric , Elbekshi Amer 
Published: October 2014

Abstract
During drilling of a well, the formation is exposed to mud filtrate invasion. The invasion displaces oil in the vicinity of the wellbore, much like a small water flooding experiment in the case of immiscible mud filtrate and formation fluid. Pressure transient or flow test of a wireline formation tester (WFT) commonly provides reservoir properties under the assumption of single-phase flow. However, a WFT sampling operation in a multiphase flow environment gives an opportunity for determining related properties in an inversion workflow by utilizing recorded bottom-hole pressure and water-cut data. In this paper, we present a novel methodology to estimate multiphase flow properties with the help of numerical simulation and optimization.
The numerical simulation model for mud filtrate invasion and cleanup consists of a proper definition of reservoir properties as well as WFT tool geometry, including size and shape of flow inlets, along with tool storage and fluid segregation effects. The model is embedded in an optimization workflow and relative permeability curves, damage skin and depth of mud filtrate invasion are then estimated by minimizing a misfit function between measured and modeled pressures and water-cuts. The relative permeability curves are parameterized using industry accepted models. The optimization workflow uses a distribution function of response parameters where the entire parameter range is included in the numerical runs, thus ensuring that a global optimum is found. Initial parameter estimates for the optimization process are determined from open hole logs, such as resistivity, and from pressure transient analyses.
The methodology developed in this paper is validated by application to a synthetic dataset with a known solution, and it is subsequently demonstrated on actual field data from a sampling job in an oil-water transition zone. The results of this paper demonstrate that it is possible to reliably estimate multiphase flow properties with defined confidence intervals from WFT sampling data. The key contributions of this study are to show the capability of estimating a variety of multiphase flow properties from routine WFT cleanup data and to establish an automated approach, including a novel inversion methodology, to reduce the turnaround time.
Data Acquisition and Processing of Carbon Rod Conveyed DTS and DAS in Very Long Horizontal Wells: First Trial in North Sea Danish Sector
Authors: Danardatu Heru , Gregersen Søren , Altern Eirik , Pellegrini Isabelle 
Published: October 2014

Abstract
This paper describes the planning, execution, data acquisition and processing of Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) conveyed by patented semi-stiff Carbon Rod (CR) in very long horizontal wells. The primary interest in CR starts from its dual nature as a conveyance method and an acquisition tool; it is able to access long horizontal sections and record meaningful flow profiling data. CR presents viable alternative to Coiled Tubing (CT) or tractor conveyed wireline logging tools. When a point pressure and temperature tool is required, it can be conveyed at the end of the CR.
This paper provides information on the preparation and execution of a three wells campaign in the Halfdan field, the first in the North Sea Danish Sector. The CR-conveyed DTS and DAS technology has been successfully and safely tested for the first time in this field. The CR was run in two injector and one producer wells. The campaign has proven the technology advantages and highlighted some learning opportunities. This paper will also describe the processing and visualization of the Temperature and Acoustic profiles and the challenges involved in handling a large volume of data (Terabyte scale).
The semi-stiff CR sensing tool consists of the CR (15 mm in diameter) itself and a bottom-hole assembly (BHA). Embedded in the CR are six optical fibers. Two multi-mode fibers are dedicated to DTS and one single-mode fiber to DAS. The three remaining single-mode fibers are dedicated to Point Pressure, Point Temperature and Vibration measurements. The Fiber Optic (FO) point Pressure (P) and Temperature (T) sensors are located in the BHA. The system provides temperature and acoustic profiles along the rod during the operation. These sensors, in conjunction with surface read out and bottomhole gauge data, provide a comprehensive tool for real-time surveillance and dynamic well analyses.
Although hole conditions were challenging, with the first well exhibiting a great deal of scale that impeded a smooth descent, CR was successfully deployed into the three wells. The rod was pushed into the horizontal sections with a reach performance equivalent to that achievable by equivalent small diameter coiled tubing. The maximum reach of CR was more than 15,400 ft MD, with a record of more than 5,100 ft reach into the horizontal section, without using a well tractor or CT to assist deployment. The sensing campaign produced more than 50 TB of well data.
The interpretation results will be presented in a subsequent publication. The aim of this paper is to serve as a reference in the planning of CR deployed DTS and DAS applied for well and reservoir surveillance.
Production Management Decision Analysis Using AI-Based Proxy Modeling of Reservoir Simulations – A Look-Back Case Study
Authors: Mohaghegh Shahab. D. , Abdulla Fareed 
Published: October 2014

Abstract
Performing look back studies to evaluate the economic and technical impacts of filed management decisions, is not a common occurrence in our industry. Even when such studies are performed, the results are hardly ever published for evaluation and scrutiny by the larger community of industry professionals. This paper presents such a study in the case of a mature giant oil field in the Middle East.
This prolific mature asset that includes more than 160 production wells has been the subject of peripheral water injection for many years to maintain pressure and help displace oil toward the production wells. Production was restricted to 1,500 bbls of fluid per day per well to avoid excessive water production as well as pulling oil from an over produced overlaying prolific reservoir. In 2005 a reservoir management study was commissioned to evaluate the impact of rate relaxation in this asset. The objective was to explore the likelihood of increasing oil production from the asset while minimizing the possibility of increasing the water cut.
The study was performed using the existing, history matched reservoir simulation model. To maximize the utility of the numerical model an AI-Based proxy model called Surrogate Reservoir Model (SRM) was developed and used for the study. Upon completion of the study (during the second quarter of 2005) the SRM ranked all the wells based on the probability of success1, once the rate relaxation program is implemented. In January 2006, management issued permission for the rates in 20 wells to be relaxed.
Using actual field data, this paper reports and evaluates the results and the consequences of the field management decisions, more than five years after their implementation. The approach and methodology used in this project will help reservoir and production managers, engineers and modelers make the most of the tools that are at their disposal to make more informed field management decisions. This paper demonstrates that using the right tools and strategies, skepticism about reservoir simulation models can be addressed effectively and can result in highly successful practices.
Groningen 2nd Phase Compression Project
Authors: Liow E. , Goorhuis H. 
Published: October 2014

Abstract
A Double End Driven Compression train with Active Magnetic Bearings (AMB), a novel concept on its own, has been chosen as the solution for the Groningen 2nd phase compression project. A development assurance program was followed by a team comprising of both Shell and Siemens specialists with the intent of ensuring that technical risks were identified and addressed as far as possible prior to field implementation. A demonstrator compression train has been installed and tested on 1 of the 20 Groningen clusters to allow early operational experience to be gained and verify its reliability. It is currently in its first year of operation and has displayed high availability and reliability.
The paper will describe:
The concept evaluation and selection The creation of the development programme to assess technical risks Technical challenges and learnings Current status of the development and design maturity
The Use Of Reservoir Simulation In Estimating Reserves
Authors: Jones A. D. , Denelle F. R. , Lee W. J. , MacDonald D. G. , Seiller B. J. 
Published: October 2014

Abstract
This paper proposes an approach for assessing a reservoir simulation model for use in estimating reserves. A simulation model can integrate complex static data, the physical description of displacement processes, production constraints and schedules. Hence it can provide important input to business decisions and to reserves estimation. Confidence in simulation predictions depends on the strength of evidence for the input data, quality control of the model, robustness of the history match, and whether there is independent evidence supporting predictions. We explain the principles for evaluating a simulation model and propose requirements for simulation predictions to be considered as proved reserves. This involves evaluation against different strands of evidence e.g. static and dynamic characterisation, wells and facilities description, reservoir performance and analogues. Simulation models are often built to support business decisions using best technical estimates for inputs. There can be instances where a simulation model may be reasonable and reliable but it only represents a‘best technical’ outcome. There may not be sufficient evidence to count the whole predicted recovery as proved reserves. We propose how such a model may be modified to also provide proved reserves estimates. The approach is illustrated through a case study which shows how the principles may be applied with different available data and at different stages of field life.
Multiphase Flow Pattern Recognition in Horizontal and Upward Gas-Liquid Flow Using Support Vector Machine Models
Authors: Li X. , Miskimins J. L. , Sutton R. P. , Hoffman B. T. 
Published: October 2014

Abstract
Multiphase flow occurs in wellbores during the production of oil and gas. Depending on the physical forces and interactions acting on different phases, there can be various phase distributions in the pipes, known as flow patterns or flow regimes, such as bubble flow, slug flow, annular mist flow, and stratified flow. Because multiphase flow pressure gradients change significantly with different flow patterns, the flow pattern prediction is usually the first step before any pressure drop estimation is performed. Moreover, in gas production wells, flow regime prediction can help engineers to determine the continuous phase to deal with liquid loading problems.
Many efforts, including correlation fitting, fluid dynamic calculation, and back-propagation neural network models, have been used to match experimental observations, which are usually presented as flow regime maps. However, there are often mismatches or errors between the prediction results and the experimental data. To avoid such matching errors, this study applies Support Vector Machine (SVM) models to directly represent the measured experimental data. If the assumption is made that there is no error in the experimental data, the SVM models always give correct output results. An SVM model is a mathematical model that is popularly used for pattern classification and nonlinear regression.
For producing oil and gas wells, horizontal and upward multiphase flow is studied in this paper. Experimental data was collected from literature and other sources in order to train the SVM models. Different flow regimes are divided by the boundaries created by the trained models. The model prediction results are plotted in 3-D plots, which provide a clear visualization of how the well inclination angle affects the flow regime transition. The SVM models also perform interpolation approximation to predict the flow regimes at various inclination angles where no experiments have been conducted. Well trained SVM models can be conveniently used and easily combined with pressure loss correlations to calculate pressure drops in wellbores. Finally, an approach using the trained SVM models to deal with liquid loading problems in gas production wells is presented.
Optimization of Well Placement in Real-Time Production Optimization of Intelligent Fields with Use of Local and Global Methods
Authors: Temizel Cenk , Purwar Suryansh , Urrutia Karelis , Abdullayev Azer , Adnan Farashikin Md , Agarwal Amit , Garcia Alejandro , Gorucu S. Emre 
Published: October 2014

Abstract
With recent advances in technology and because of its application in the oil industry, real-time production optimization in intelligent fields has become more practical. The optimal control methods developed previously are now applied in smart workflows to maximize recovery or net present value (NPV). Development of new and mature fields can be improved with integrated workflows that account for the technical and economic aspects of hydrocarbon recovery, as well as the potential impact of uncertainty. This approach is important for a high-impact decision-making process such as well placement.
Optimization of well placement in complex reservoirs requires a thorough understanding of both local and global optimization methods, key parameters, and constraints. A major drawback in this process is that most optimization algorithms are incorrectly understood and applied by the engineers. In this paper, that gap is addressed by presenting not only the theoretical background but also the application of optimization algorithms existing in workflows that couple reservoir simulation with commercial optimization and uncertainty software updated with continuous data flow from the field.
Different optimization techniques are used for optimal well placement. Some of the most commonly used techniques are exploratory, direct, and gradient optimization methods. This paper provides a thorough analysis of these approaches and illustrates the performance evaluations using reservoir simulation case studies as applied in intelligent fields. The analysis considers the computational complexity, scale of the problem, and definition of the objective function. The performance evaluation builds on the analysis to compare the optimal well location, time of convergence to optimal solution, and the corresponding reservoir simulation results. The intended application of this study is to assist in selecting the right approach to make sound engineering decisions for substantial improvement in field production rather than selection of methods which may lead to decreased ultimate recovery.