bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Smart Flow for SmartWells: Reactive and Proactive Modes
Authors: Carvajal G. A. , Boisvert I. , Knabe S. 
Published: June 2014

Abstract
Interval Control Valves (ICVs) are important tools for managing production challenges such as localized water or gas breakthrough, particularly in multilateral wells and horizontal wells with open-hole completions. This paper describes an automated workflow designed exclusively for wells with intelligent completions that use ICVs and real-time sensors for both down-hole and surface data. The workflow was designed to provide right-time production monitoring, well diagnostics, and ICV optimization for those intelligent wells equipped with ICV and multiple downhole pressure and temperature sensors.
The automated workflow performs several tasks: collect and condition production data, provide local history matching, generate several optimization iterations to estimate the best valve setting, and use numerical simulation to generate a number of scenarios where the ICV positions are changed over simulation time. To perform these tasks, the workflow models fluid mechanics between the ICV and a heterogeneous reservoir using next generation software such as steady-state hydraulic applications and a numerical simulator that accounts for the ICV settings. Tracking and visualization of streamline trajectories was used to add value of optimization process particularly the calculation of well distribution factors.
The horizontal well in this study has 5 ICVs positioned at different locations. The reservoir model is dual porosity-dual permeability. Local Grid Refinement (LGR) is used to model the horizontal section. The reservoir is subjected to a waterflooding process with more than 10 years of history. Two injectors are associated with a horizontal producer.
The workflow operates in two ways: reactive mode and proactive mode. Reactive mode is activated when water cut increases and oil rate or flowing bottomhole pressure (BHP) decreases, at which point the workflow performs local history matching to update the well performance model and optimize the settings of the 5 ICVs to obtain the minimum water cut and maximum oil rate at given constraints. Proactive mode occurs every month or quarter when the numerical simulation runs hundreds of scenarios to obtain the best combination of ICV settings to maximize oil recovery and minimize water injection and production.
The workflows described in this paper demonstrate the potential to manage ICV settings to substantially increase oil production while managing water influx.
Real-time Diagnostic Analysis of Artificially Lifted Wells: A Smart Workflow Approach
Authors: Agarwal Amit Kumar , Purwar Suryansh , Bravo Cesar Enrique 
Published: June 2014

Abstract
The value proposition of petroleum workflow automation traditionally includes the ability to provide comprehensive production surveillance to streamline efficiencies and avoid unwanted downtime. But this is just the first step in a production workflow automation strategy. The ability to analyze flow measurements and other operational data in real time to continually diagnose and optimize well performance is a major step forward. In its current setup, the proposed method can be applied to continuous gas-lift and ESP-operated oil wells. A central problem when operating gas-lift and ESP wells is to identify and address issues causing underperformance at the correct time. The purpose of this paper is to describe a method for real-time analysis of the performance of artificially lifted wells and then accelerate and improve the diagnosis and decision-making processes. The solution presented here can operate in complex, high-frequency data environments and has the ability to federate multiple data sources. The method is based on proven steady-state nodal analysis techniques and models multiphase flow from the reservoir to the wellhead. The well model is updated, and the model results are displayed as soon as new data arrive, thereby providing the users with the most current information from their well. Available data include pressure gradient curves, inflow and outflow curves, gas-lift or pump performance curves, the current model operating point, and the recommended opportunity point. The real-time measurements combined with the real-time model calculations provide an advanced tool for artificial lift system analysis and troubleshooting. This method is designed to generate actions, such as decreasing or increasing the gas injection rate, pump frequency, and choke setting. The ultimate benefit is the ability to identify performance issues and opportunities at the correct moment in time.
Real-Time Rate of Penetration Optimization Using the Shuffled Frog Leaping Algorithm (SFLA)
Authors: Yi Ping , Kumar Aniket , Samuel Robello 
Published: June 2014

Abstract
The increasing complexities of wellbore geometry imply an increasing well cost. It has become more important than ever to achieve an increased rate of penetration (ROP) and, thus, reduced cost per foot. To achieve maximum ROP, an optimization of drilling parameters is required as the well is drilled. While there are different optimization techniques, there is no acceptable universal mathematical model that achieves maximum ROP accurately. Usually, conventional mathematical optimization techniques fail to accurately predict optimal parameters owing to the complex nature of downhole conditions. To account for these uncertainties, evolutionary-based algorithms can be used instead of mathematical optimizations. To arrive at the optimum drilling parameters efficiently and quickly, the meta-heuristic evolutionary algorithm, called the ‘Shuffled Frog Leaping Algorithm,’ (SFLA) is used in this paper. It is a type of rising swarm-intelligence optimizer that can optimize additional objectives, such as minimizing hydromechanical specific energy.
In this paper, real-time gamma ray data are used to compute values of rock strength and bit-tooth wear. Variables used are weight on bit (WOB), bit rotation (N), and flow rate (Q). Each variable represents a frog. The value of each frog is derived based on the ROP models used individually or simultaneously through iteration. This optimizer lets each frog (WOB, N, Q) jump to the best value (ROP) automatically, thus arriving at the near optimal solution. The method is also efficient in computing optimum drilling parameters for different formations in real time. The paper presents field examples to predict and estimate the parameters and compares them to the actual real-time data.
Strategic Well Test Planning Using Random Forest
Authors: Aulia Akmal , Rahman Aulia , Quijano Velasco Juan Jose 
Published: June 2014

Abstract
Optimizing a huge oilfield with hundreds of wells would involve thorough analysis. Here, the performance of a subset of those wells might be a critical factor. Therefore, it is important to obtain accurate information on that subset.
Obtaining accurate information on an individual well is usually done via a proper well test analysis. Although performing a well test is relatively expensive, it can provide important information such as the skin, reservoir pressure (i.e. BHP), permeability, and nearby faults.
In this study, we would like emphasize on the measurement of BHP. This emphasis is chosen due to the fact that the BHP from a subset of all wells can be a contributing factor towards the field oil recovery factor. Hence, the objective of this study is to find that subset of wells based on uncertainty studies conducted via reservoir simulations.
Here, the uncertainty study involves performing 100 Latin Hypercube Monte Carlo (LHMC) sampling of the reservoir simulation input parameters. For each of the 100 runs, the uncertain input parameters and simulated BHP values/trends for each well are recorded. Thus, a dataset consists of the input parameters, simulated BHP for each well, and the field oil recovery factor as columns will be generated.
The random forest (RF) algorithm was implemented in order to find the subset of wells mentioned. RF algorithm is simply a collection of decision trees (DT), where the results generated by RF were obtained by using a voting mechanism among the trees involved. Several trees were sampled from the generated forest, and reveal interactions between the monitored BHP value/trend with other parameters. Combination of LHMC and RF can also be seen a global approach to sensitivity analysis, since RF can reveal the rank of importance of the RF’s independent variables based on a collection of uncertainty runs.
In the exploration and production (E&P) applications, DT has been used extensively; appraising, real option valuation selecting an artificial lift method, predicting permeabilities and detecting fracture corridors. However, to the best of the authors’ knowledge, RF had never been implemented in any E&P activities, particularly in strategic well test planning.
Standardized Reporting of Hydraulic Fracturing Treatments Supports Production Optimization and Meeting Regulatory Requirements
Authors: White Ky W. , Lamborn Raymond , Svensson Inge , Sui Xiaojing , Sanders Steven , Thorsen Arve , Parks John , Hughes Jeffrey A. , Hamilton Fiona , DeSantis Cory , Fore Keith , Slack Ronnie 
Published: June 2014

Abstract
Hydraulic Fracturing treatments generate large volumes of valuable data. The industry recognizes this data can be leveraged to optimize various parameters for oil and gas production from a well. Additionally, there are evolving regulatory reporting requirements that require a standardized exchange of information with the service providers and the regulatory organizations. Historically, data was provided to the well operator in custom paper or varying digital formats, resulting in delays, overhead, and inconsistent information during the database import process.
In response, the Energistics© global consortium 1 united a wide variety of industry professionals to develop the stimJob Object open exchange standard for hydraulic fracturing in November, 2010. The stimJob Object standard was subsequently updated and incorporated into version 1.4.1.1 of the WITSML™ (Wellsite Information Transfer Standard Markup Languag) standard as released in July, 2012.
The stimJob WITSML 2 report was initially deployed to the high volume USA market as a post-job report for each stage of the hydraulic fracturing operation. It is an XML file containing a data record of the treatment including rates, pressures, volumes, densities and additives. The digital data format conforms to WITSML-compliant requirements for use in several commercial applications. The stimJob report utilizes the current version of WITSML, v1.4.1.1, which is a further refinement of the data-object specifications for ease of implementation and use. WITSML is designed for the seamless flow of well data between the operators, service companies, and regulatory agencies, to speed and enhance decision making and reporting.
This paper outlines the content of the stimJob report, how the data is exchanged with the operator and the beneficial results from the early adoption of the standard into the stimulation workflow.
Digital Oilfield Implementation in Naturally Flowing Oil Fields with Increasing Water Encroachment: One Company's Experience in Burgan Field
Authors: Al Jasmi A. , Choudhuri A. , Joy D. 
Published: June 2014

Abstract
Kuwait Integrated Digital Field Gathering Center 1 (KwIDF-GC1) pilot project was launched in Burgan field in 2009 as an investigation into how a cross-functional and cross-domain infrastructure could be established to aid in the achievement of corporate goals set for the following two decades. The company’s vision for 2030 includes a philosophical shift in the way that the country’s workforce accomplishes its tasks, employing latest technologies and work processes. The project solution integrates field instrumentation with workflows automated in software and focused collaboration.
All well sites were instrumented with pressure and temperature gauges, multiphase water cut meters, remotely automated chokes and electronic H2S detectors. Automation of field was the first step in providing the advanced technology required of this project, realizing tangible advantages in minimizing the health, safety and environmental (HSE) exposure of field personnel. Well site data can be read, and choke positions can be set, remotely at the gathering center without the need for field personnel to enter hazardous areas. The implementation is digital throughout, employing a field-level WiMAX radio system to provide fast and secure Ethernet-based communication from gathering center to the wellhead. Work processes were converted into automated digital workflows supported by advanced network modeling and nodal analysis software in a state-of-the-art collaboration center. This paper presents framework of the pilot project demonstrating the hardware and software tools that have been used to optimize production of the Burgan field assets as a part of the KwIDF-GC1 project. The concentration of this paper will be a detailed discussion on designing of the field instrumentation system.
From Market Basket Analysis to Well-head Monitoring: Using Events to Increase Oil Recovery
Authors: Bailey R , Lu Z , Shirzadi S , Ziegel E 
Published: June 2014

Abstract
BP has a successful, limited scope deployment of a non-parametric capability called Event Detection and Association (EDA). The technology resembles ‘Market Basket Analysis’ tools used to carry out associative assessments of item sets. Variations from the original analytical approach have been found necessary before the EDA techniques could be used to solve real-world oil and gas problems. These changes are based on fundamental physical laws such as conservation of mass and the recognition that systems of relevance all contain time delays between the ‘items’ that are to be tested for association. EDA comprises part of a suite of tools collectively referred to as Top Down Waterflood Diagnostic and Optimisation (TDWF). TDWF is a top down reservoir simulation toolkit capable of developing a statistically valid reservoir representation based purely on operational data together with a limited set of configuration data (the x, y location of wells is required). EDA contributes an independent validation of the Capacitance Resistivity Model (CRM) using the same data further processed to extract causal events on the injectors and delayed-response events on the production wells. By studying the time based correlation of these input and output events the basic TDWF CRM model is either confirmed or has suggestions made for improving its predictive capability.
Early studies on three reservoirs, when combined with insights from suppliers of four different data-driven technologies, confirmed the robustness of EDA. A limited trial deployment followed in four operating regions. The users’ studies have generated insights and improved the product design and capabilities. EDA can deliver insights into well connectivity in the subsurface. Working from data alone EDA can even approximate the findings of a much more expensive tracer study and deliver these insights in a very short time and at a fraction of the expense.
Big Data in Exploration and Production: Silicon Snake-Oil, Magic Bullet, or Useful Tool?
Authors: Cameron David 
Published: June 2014

Abstract
Big data is a nebulous and much-hyped concept in IT. IT companies have noted that exploration and production involves handling and storing large, and ever-increasing, amounts of data. They are therefore positioning their various "big data" products as solutions in search of E&P problems. This paper presents an overview of big data technologies and discusses ways in which these technologies could be used to improved exploration and production operations. The paper will present a working definition of what big data is for the upstream oil and gas industry. Big data is a repackaging of a variety of IT technologies, including pattern analysis and recursive estimation using streaming data, management of large-scale parallel databases, in-memory computing, fast search, natural-language processing and the statistical analysis of large datasets. In many ways, the E&P industry has been a pioneer in these areas, where the technology push has been driven by geophysicists and engineers rather than computer scientists and statisticians. The message presented is that these tools give allow faster processing, searching and analysis of data, but the their full benefits will only be obtained when they are (a) combined with physical insight and models, (b) used by talented and experienced engineers and (c) deployed to solve real and pressing business problems and (d) integrated with corporate IT. The paper will discuss the organizational and ethical implications of big data, including issues such as: the use of big data in small organizations; the roles of IT companies and traditional service companies in implementing big data; the maintenance of security, privacy and accountability using big data and the use of big data in collaborative workflows and integrated operations.
The Future of Telemedicine in O&G
Authors: Thorvik K. , Nystad A. , Skogås J. G. , Fernandes A. , Reegård K. , Simensen J. E. , Rindahl G. , Silva E. , Bergsland T. , Klingsheim O. , Evjemo T. E. 
Published: June 2014

Abstract
This paper presents an ongoing work in on the future of telemedicine in O&G. There has been a huge development in the use of video consultation between remote patients and the doctors. We believe the future of telemedicine in O&G will add to this workflow by investigating how we can transfer visual medical data between "offshore nurses" and "medical experts" at hospitals onshore in order to improve diagnostics and treatment. We will describe a decision support system that supports an optimal workflow and collaboration, between medics onshore and offshore. The goal is to make better and faster medical decisions, and improve the quality of healthcare offshore.
The oil companies have much of the same structure and same challenges in remote medical treatment. We investigate an optimal workflow including how technology supports a new telemedicine work process by transmitting very high quality information (e.g. ultrasound images) to the cardiovascular medical experts.
We will review our work on developing a prototype "on the go" solution between medics offshore and the medical experts onshore at the hospital. The concept will be based on a Pad/PC solution capturing the ultrasound image transmission between the user and experts, a systematic work process and a knowledge base integrated in the Pad/PC "on the go solution". With optimal workflow it should not take more than 5-7 minutes from the starting point to have a decision from the medical expert. This will improve diagnostics, medical safety and health quality on offshore installations.
Geographically-Distributed Databases: A Big Data Technology for Production Analysis in the Oil & Gas Industry
Authors: Preveral Aymeric , Trihoreau Antoine , Petit Nicolas 
Published: June 2014

Abstract
The paper discusses some reported shortcomings of state-of-the-start IT technologies currently employed in the data management of Oil & Gas production operations. Most current IT infrastructures connect historian databases, production databases and application servers. This creates complex issues of data consistency between these systems. In the discussion, a particular focus is put on the geographically-distributed nature of the network which suffers from low-bandwidth limitations and un-reliabilities, e.g. due to satellite communication links.
Taking the production engineers’ viewpoint, an example of production allocation using Data Validation and Reconciliation (DVR) serves to stress the malicious impacts of the described architecture. Production allocation represents one of the various monitoring and analysis tasks that are performed, on a daily basis, at the centralized level of data management systems. A quantitative study shows that the problem of mis-synchronization of databases is of great practical importance.
We propose solutions to improve the robustness to communication outages. To improve data consistency across sites in a decentralized manner, the paper exposes the key concepts of distributed storage, message-based communication, and clustering. More generally, the paper proposes to shine a light on the potential relevance of several recent advances in the scientific field of "big-data" to the world of Oil & Gas upstream industry. These off-the-shelf technologies must be specifically tailored to geographically-distributed networks. The specificities are detailed, the necessary development work is outlined, and the potential qualitative benefits are estimated. A possible implementation is sketched.
From a Daily Drilling Report to a Data and Performance Management Tool
Authors: Dawson Colin , Verkuil Harry 
Published: June 2014

Abstract
Using historical data to aid the planning of future operations is a very promising area of data analytics. Many organisations have their own data reporting tools alongside a number of benchmarking tools for use in performance analysis. One commonly used tool for both purposes are locally installable spreadsheet packages. In using this type of tool, the ability to analyse data and communicate with other applications is very limited. This limitation results in less accurate performance analysis, which leads to inaccuracy and less detailed analysis.
Using a number of different methods/tools for the same purpose is a problem that is heavily embedded in today’s Oil & Gas culture. This not only doubles or trebles costs, but leads to a sparsely populated and vague data model that cannot be used effectively for data analysis.
By standardising the way data is recorded, the process can be altered so that input data is recorded in one single entity and any analysis can be performed from one place. Using this model, the process can be expanded from Internal-Regional benchmarking to Internal-Global benchmarking, meaning that global data can be logged in one place and the same model used for all analysis.
Structure becomes very apparent in Daily Drilling Reporting. Many companies use very different applications, formats and codes to report data. This lack of standarisation causes a number of problems.
‘How can data that is logged with different degrees of accuracy be compared?’
For example, if one solution logs all BHA time under a single entity ‘BHA’ and another logs individual segments for ‘Making up BHA’, ‘Run in hole BHA’ and ‘Pull out of hole BHA’, how can time be sliced in the first solution so that each category can be analysed accurately?
Through actively streamlining all input data to a managed, centralised database, data analysis and performance engineering can be improved, so that future well planning can be improved. This can clearly result in cost reduction and performance improvement.