bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Data Quality Control of Real-time Rig Data
Authors: Arnaout A.. , Zoellner P.. , Johnstone N.. , Thonhauser G.. 
Published: June 2014

Abstract
There is an increasing requirement for centralized analysis of drilling data to improve performance. Typically, data from service companies is transmitted from the rig to the clients Real Time Operating Centre (RTOC). A major problem is the quality of this data, which can lead to incorrect analysis and a breakdown in trust between the rig and the RTOC. Currently real time data is, at best, only checked for completeness.
In this work, we present a model for data quality control in real-time operating centres. The following points are considered in our suggested model: Monitoring the quality of real time data for:CompletenessContinuityTimelinessValidityAccuracyConsistencyIntegrityAlarming of all unexpected data;Generating daily data quality reports.
The suggested data quality control model has different groups of key performance indicators, calculated in real time from the streamed data. The model then takes the values of these performance indicators as input, and evaluates different properties of data quality as the output. Additionally, a new group of intelligent model-based key performance indicators is suggested. These indicators give the possibility to monitor actual quality and measure it against the expected one. The result is a measurement of Quality of Service (QoS) supplied by data provider at the rig.
Quantifying the Intelligent Field Added Values
Authors: Husain Z. B. , Al-Hajri M. A. 
Published: June 2014

Abstract
Saudi Aramco has embarked on an aggressive plan to implement the Intelligent Field infrastructure and workflows across all existing and new fields. Since the inception of the first Intelligent Field in 2003, the technology and processes have provided obvious benefits to Saudi Aramco and provide clear justification for a business case in favor of the Intelligent Field. Even with such obvious gains through the Intelligent Field implementations, very few, if any, attempts have been made to quantify the value and benefits of the program implementation in dollar terms. The exercise in quantifying the value is considered a step toward sharing the lessons learned within the dynamic environment of the energy sector. It also helps with understanding how new and improved sensors and communication technologies have combined with people and processes to transform the way business is done currently.
This paper discusses some of the reasons why the value determination in the Intelligent Field environment is such a challenge and why such an effort raises questions from all sides and how we can present more of such cases from around the world. Additionally, this paper will present some convincing cases, more relevant to the production engineering and operations environments, quantifying the benefits in dollar terms. By making comparisons between conventional fields and fully implemented Intelligent Fields using cases from within the company, the author highlights the value for the unconvinced individuals who still consider the Intelligent Field as somewhat of an additional load on their already stressed work schedules, where an overwhelming amount of data is available and where, sometimes, the most critical data is not available instantly.
Selection and Optimization Study on Passive Inflow Control Devices by Numerical Simulation
Authors: Zeng Quanshu , Wang Zhiming , Yang Gang , Wei Jianguang 
Published: June 2014

Abstract
In long horizontal wells, production rate is typically higher at the heel than that at the toe. The resulting imbalanced production profile may cause early water or gas breakthrough into the wellbore. Once coning occurs, well production may be severely decreased due to limited flow contribution from the toe. To eliminate this imbalance, inflow control devices (ICDs) are placed in each screen joint to balance the production inflow profile across the entire lateral length and to compensate for permeability variations.
Currently, there are three different passive ICD designs in the industry: nozzle-based, helical channel, and tube-type. They use restriction mechanism (nozzle-based), friction mechanism (helical channel) or cooperating the two (tube-type) to achieve a uniform inflow profile. However, the reality is that none of these ICDs alone meets the ideal requirements of an ICD designed for the life of the well: high resistance to both plugging and erosion, high viscosity insensitivity, and high density sensitivity. Therefore, the selection and optimization of ICDs for a specific reservoir requires for further study.
In this paper, three computational fluid dynamic based, numerical models of these ICDs with same flow resistance rating (FRR) were developed to characterize the flow performance. The results show that the throttle pressure drop depends on fluid properties, and geometries of each ICD. The weight of each factor that affects pressure drop was determined by maximizing deviations combining with analytic hierarchy process, and subsequently optimal ICD under specific reservoir condition was selected with the help of fuzzy comprehensive evaluation, thereby an ICD selection diagram was built. For a specific reservoir, we will have the selected ICD with best pressure drop composition by optimizing its structural parameter, which has best corrosion resistance and least viscosity sensitivity.
On The Stochastic Response Surface Methodology For The Determination Of The Development Plan Of An Oil & Gas Field
Authors: Valladão Davi M. , Torrado Ruben R. , Flach Bruno , Embid Sonia 
Published: June 2014

Abstract
The economical performance of an oilfield operation is uncertain and highly influenced by strategic and operational decisions variables such as well placement, scheduling and control. Based on numerically intensive reservoir simula- tors, the evaluation of an extensive list of possible decisions across all possible realizations becomes computationally intractable and additional mathematical techniques are required. A common approach to dealing with this problem is the Response Surface Methodology (RSM) coupled with Design of Experiments (DoE) and sampling techniques. Existing approaches to construct surrogates depend on specific statistical/risk measures such as expected value and standard deviation. For example, in order to construct a surrogate for the standard deviation of NPV, one would compute the standard deviation associated with the simulation results over the selected geological realizations for each candidate production strategy and then fit a mathematical model to it. In this case, the idiosyncratic response of each geological realization with respect to the production strategy is lost, which may lead to a bad risk assessment and, consequently an inappropriate decision making process. In this paper, we propose Stochastic Response Surface Methodology (SRSM) to enhance the decision-making process over the determination of oil & gas production strate- gies while properly taking into consideration geological uncertainty. The SRSM does not depend on any pre-defined risk measure providing the necessary flexibility to evaluate the intrinsic risk-return trade-off associated with the economical performance of the reservoir. Our approach is based on the construction of surrogates for each geolog- ical realization selected by sampling procedure. We argue that constructing a different surrogate for each selected realization captures the idiosyncratic behavior of each representative geological setting and provides the flexibility of choosing any set of risk measures after the surrogate construction has been done. Based on the Brugge field, an SPE benchmark case study, we provide a numerical example to illustrate our methodology.
Advanced Optimization Techniques Applied to a Petrobras FPSO
Authors: Gunnerud Vidar , Ursin-Holm Stine , Furtado Teixeira Alex 
Published: June 2014

Abstract
In this paper, a new method for production optimization is tested on a Petrobras offshore floating production storage and offloading (FPSO) unit from Campos Basin, Brazil. The FPSO/platform has two subsea templates, with 7 and 3 wells respec- tively. In addition there are 7 topside wells. Both of the subsea templates have two pipelines connected to the platform, one production and one test pipeline. The purpose of the optimization method is to find the optimal routing of well streams, choke settings and gas lift rates for all wells such that total production of oil is maximized. The method makes sure that the pressure drops throughout the system are feasible and gas available for gas lift is limited and needs to be distributed properly among the wells. Further, it takes into account that the handling capacity of gas, water and liquid is limited and make sure that this is obeyed.
The method finds the optimal solution to this problem in less than 20 seconds, which are two orders of magnitudes faster than the current state-of-the-art methods available on the marked. This enables a totally different production engineer workflow, since instant re-optimizations could be conducted on-the-fly during e.g. a well optimization meeting. It essentially automates the what-if study conducted to find the best choke, routing and gas lift settings, and enables the production engineer to focus on other important questions like; what is the preferred operational envelope for each well, and is the inflow performance ratio predicting the well behavior accurately for the recommended (optimal) production strategy, etc.
The computational speed-up is achieved by utilizing the structure of the production network, and by using efficient well and pipeline proxy models inside the optimization algorithm. The paper outlines the principles of the optimization method and how it was applied to a real field problem. Further, it reports on the results and discusses the potential benefits of such a method.
Implementing Intelligent Energy - Change Management Experiences and Recommendations
Authors: Halland Thomas , Jensen Leif Kristian , Eike Martin , Solnørdal Dag 
Published: June 2014

Abstract
Over the last decade, change management has had significant focus in Statoil, and we have implemented a large number of major changes to our operational work processes in order to improve decision support and drilling efficiency. Deployment of new technology and new processes utilizing real-time drilling data affect not only Statoil as operator, but also service companies, contractors, application vendors, and standardization committees.
Implementing internal changes affecting day-to-day operations is in itself difficult. Implementing changes also affecting and changing other companies' processes and deliverables are even harder.
Change management in Statoil have had many different approaches with regards to implementation and results. In hindsight we see that some of the transitions have been very successful, whilst others have been more challenging. We also see that the objectives and characteristics of the various transitions heavily influence the optimum change management process. Small differences in the approach can drastically influence the outcome.
In this paper we will present some of our observations, learning's and experiences implementing new processes, new technology and new contracts within the domain of real-time drilling data. We will touch upon the following topics: Optimizing the collaboration: operators, service companies, contractors.Change management focus: Functionality vs. technology.Commercial technology vs. CustomizationsContract strategy
The cases in this paper will illustrate each learning experience, both good and bad.
Finally, we will present a summary of our experiences and recommendations, hoping that this may be of value to others working with change management processes within the exciting area of Intelligent Energy.
Development and Utilization of Integrated Artificial Expert Systems for Designing Multi-Lateral Well Configurations, Estimating Reservoir Properties and Forecasting Reservoir Performance
Authors: AlMousa Talal s. , Ertekin Turgay 
Published: June 2014

AbstractReservoir simulation is one of the main if not the most important tool reservoir engineers use to forecast a reservoir performance. Nevertheless, developing and operating a reservoir simulator tool in the first place can be an arduous task that requires a set of highly skilled individuals in science, advanced mathematics, programing, and reservoir engineering and powerful computational models (Ertekin, et al., 2001). The reliability of a reservoir simulator depends on the availability and the quality of the reservoir properties. These properties are obtained from open-hole logs, core studies and well testing analysis which can sometimes be prohibitively cost intensive. Another important component of the overall process affecting the reservoir performance is the multilateral well configuration. Achieving the right design of a multilateral well configuration is a complex problem due to the vast possibilities of well forms that need to be evaluated. In light of the above, this paper demonstrates the development and the application of a set of integrated artificial expert systems in the area of forecasting, reservoir evaluation and multilateral well design. The applied method is implemented to volumetric single phase gas reservoirs with a variety of rock and fluid properties spanning tight to conventional sands. The developed approach delivers a proxy to the conventional numerical simulator for predicting reservoir performance, an expert system for estimating reservoir properties from rate decline data, and an expert system for recommending multilateral well design parameters for a target recovery profile. Furthermore, graphical user interfaces (GUIs) in conjunction with the expert systems structured are developed and assembled together for standalone installation. These GUIs allow the engineer to edit and input data, produce results numerically and graphically, compare results with a commercial numerical simulator, and generate an interactive 3-D visualization of the multilateral well. It is expected that the developed expert systems will immensely reduce time requirements and effectively enhance the overall decision-making process. However it is emphasized that this paper is not suggesting the replacement of existing and well established procedures, protocols and know-how with the developed expert systems, but rather applying them as auxiliary, pre- screening or complimentary systems where and when applicable to ease of the computational overload, deliver a solution to the inverse-looking problems and enhance the overall decision-making process.
Spectroscopic Sensing for In-Situ Monitoring of Water Fraction in Natural Gas Flow
Authors: Enshasy Hesham , Omar Amjad , Cheng Mosong , Alnuweiri Hussein 
Published: June 2014

Abstract
The paper presents a microwave sensing method to detect and quantify the presence of water or saline water in natural gas pipelines. Efficient and safe operation of natural gas production lines depends on intelligent monitoring capabilities of specific chemicals harmful to the operations and to the environment. For example, high salinity formation water will erode the steel pipelines, thus requiring immediate actions. Quantifying such contents is as important as detecting them. Real-time, in-situ quantification of water fraction and salinity in the flow leads to reduced production cost and improved equipment safety. The gas pipe in this work is modeled as a waveguide that is filled with gas and different fractions of water. By measuring the magnitude and phase of the reflected and transmitted signals over a broad spectrum, the water fraction can be calculated using waveguide model. With water fraction increasing, signal magnitude changes and cutoff frequency shifts. Both signal magnitude and frequency shift can give accurate estimation of the water fraction inside.
The advantage of using microwaves is the ability to cover the whole cross section area of the pipe, without missing any water droplet inside. Other methods, like electrochemical, resistivity or capacitance probes, only capture local variations. HFSS software was used to simulate the wave characteristics for different water volume fractions inside the pipe and the experimental data showed that a water fraction as low as 1% can be quantified. A repeatability study was done in this work and it showed that measurements have good repeatability.
Index Terms — Microwave Spectroscopic Sensing, NDT testing, Natural gas industry, Petroleum industry, Water fraction.
Predictive Maintenance and Deterministic Maintenance Scheduling for Continuous Process Industry Rotary Equipments
Authors: Dey Tapan , Chandramohan Sarika , Bansal Shefali 
Published: June 2014

Abstract
This paper explores and analyzes the methods for predictive maintenance and deterministic maintenance scheduling for continuous process industry rotary equipments. This paper consists of a brief introduction of various types of predictive maintenance techniques, maintenance schedules and the methodology to develop the optimization models. The paper also considers a turbine as a case example for an equipment type, analysis of results using the optimization model and conclusion from the analysis and future scope related to the optimization model. In the overall end-end flow, this paper outlines the use of analytical modeling techniques to first predict for example, the likelihood of a component of an equipment failing, the recommendations that can be generated based on the predictive model scores, and then use an optimization model to generate the maintenance schedule. The predictive model and the optimization model are modular and can also be used together or in isolation to provide different set of capabilities and advantages to the end customer.
Binary integer programming model is used to generate the maintenance schedule considering the deterministic part life, pulling back of events, non maintenance period of equipment and minimum number of outage. There are mainly two decisions in the optimization model which are occurrence of event for each part and outage for the equipment. Few of the business constraints are as part life for repair and replace, non maintenance period due to business requirement. The objective of the optimization model is to minimize total maintenance cost over time horizon.
The generated maintenance schedule using the optimization model reduces the wastage of part life and number of outage for equipment. This also increases the utilization of the equipment. This also reduces the downtime of equipment due to unscheduled and unnecessary maintenance. Over and above this minimize the total maintenance cost of the equipment. This model can be extended to stochastic model considering stochastic failure time of parts life. Net present value can also be included to calculate the maintenance cost for long term.
Production reduction is a major issue due to unscheduled maintenance. This optimization model gives an unique idea to generate the maintenance schedule for very complex machine such as turbine which has many sections, each section has many part-kit, each part-kit consist of many parts with various failure time for each type of event in a very efficient and effective way. This idea can be used for any industry which has very large and complex machine to get the maintenance schedule. Dragline in opencast mine, turbine, manufacturing industry, production industry are the suitable place to use this type of maintenance schedule.
Petroleum Development Oman Approach to Intelligent Production Measurement
Authors: Al Hanashi K. K. , Syed Mohammad Z.. , AL-Obaidani A. N. , AL-Hinai S. H. 
Published: June 2014

Abstract
Managing large number of flow meters, coupled with varying fluid properties and subsurface comingled production from multiple reservoirs, is challenging in terms of static design data and dynamic calibration data. Applying intelligent approaches to flow meters calibration and overall Production Measurement Management System can be of distinguished benefit. This paper presents examples of Petroleum Development Oman (PDO’s) intelligent approaches in remote flow meters calibration and overall performance monitoring of production measurement systems.
The first example looks at the challenge in keeping multiphase flow meters fluid and PVT data configurations up to date in a field undergoing a gas injection where gas properties are changing with time. The concept of automatic remote calibration for gas properties is discussed and field trial results are presented.
The second example describes the initiative for an integrated production measurement management system (iPMMS). By integrating engineering design data and current operating environment, the system brings transparency, reliability and integrity to all the data around a measurement system. Such a system will enable production data user to evaluate the quality of the data before making key business decisions. The system will also facilitate in managing lifecycle changes for measurement systems.
Energy Harvesting Powered Wireless Monitoring and Control in Oil and Gas
Authors: Ahmad Talha J. , Noui Mehidi Mohamed N. 
Published: June 2014

Abstract
Technological advancement in sensors, digital electronics and wireless communications have enabled development of low cost and low power wireless sensors and networks, thereby enabling a paradigm shift in autonomous monitoring and control for a wide range of applications in the oil and gas industry. These applications can be found all across the industry including supply chain, refineries/petrochemical plants, pipelines, exploration, drilling, production and transportation. One of the challenges is powering up these sensor/control devices. Despite the ultra-low power consumption of wireless nodes and the high energy density of batteries, they still have limited stored energy and, therefore, a limited lifetime. In many scenarios, the battery replacement of sensors could be a very time consuming task and even uneconomical and unmanageable. To tackle these issues and enable fully autonomous and maintenance free wireless sensing and control, a continuous source of energy is required.
We have investigated Energy Harvesting as the potential solution for providing reliable and long-term power for sensors by scavenging various ambient energy sources such as environmental/machine vibrations, thermal sources, flow, solar, wind energy and converting it to useable electrical energy. This paper provides a survey of energy harvesting techniques including mechanical (piezoelectric, electrostatic, electromagnetic and magentostrictive), thermal (thermoelectric, pyroelectric), light, and various state-of-the-art technologies. The requirements and technical challenges, along with financial drivers and benefits, are addressed. Various ambient energy sources present at the surface and their usage for different applications in the oil and gas industry are identified and discussed in detail.
Finally, recommendations are made for improvement and achieving the goal of practical implementation of energy harvesting to enable self-powered remote and wireless monitoring and control in oil and gas. Although the energy harvesting market is rapidly increasing because of its growing demand in various industries, the technology hasn’t been thoroughly investigated for oil and gas applications.