bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pressure Evolution and Production Performance of Waterflooding inn-Heptane-Saturated Fired Berea Cores
Authors: Rezaei N.. , Firoozabadi A.. 
Published: June 2014

SummaryThis work presents experimental results and interpretation of injection pressure and recovery performance of waterflooding in strongly water-wet fired Berea cores saturated with n-heptane. The experiments were conducted at constant injection rate at room conditions, and the effects of injection rate and initial water saturation on the oil-recovery performance and dynamic-injection pressure were investigated. Elements of surprise were observed in the injection-pressure data. The pressure profiles showed four distinct regimes, each governed by capillary or viscous forces. At low capillary numbers (Ca=uμ/σ&lt;10–6), capillarity governed two pressure regimes, corresponding to the core inlet and outlet. In the early part of waterflooding, pressure stayed constant for a considerable time before hydrodynamic pressure gradient could overcome the capillary pressure gradient. After viscous forces dominated, a linear increase in injection pressure over time was observed up to breakthrough time. A sudden pressure rise was observed close to breakthrough because of capillary retention at the core outlet. The pressure became constant after the breakthrough when the water- and oil-saturation distributions were stabilized. Changing the injection rate by an order of magnitude in the range from 2.2 to 22.2 pore volumes (PV)/D (equivalent to Ca = 10–7 to 10–6) did not appreciably change the oil-recovery performance; similar breakthrough time and final oil recovery were observed. The effect of initial water saturation was also investigated. When lowering the initial water saturation beyond that established in oil flooding, production performance and injection pressure were similar to those of a core without the initial water saturation. The injection pressure at breakthrough was found to decrease with increase of the initial water saturation. Waterflooding was modeled by including the capillary pressure and excellent agreement was obtained with experimental results of production and injection pressure. We find that in the absence of in-situ saturation measurements, the injection pressure is a better variable for tuning the model parameters compared with the production history alone.
Intelligent Integrated Management for New Ventures in High Risk Developing Countries
Authors: Haider Muhammad Ali , Abdullah Sheikh Muhammad , Khalid Ramiz , Mirajker Meryam , Haider Batool Arhamna 
Published: June 2014

Abstract
The world is becoming flatter through global integration and ‘smarter’ through interconnected communication and com-merce. For oil and gas organizations this means finding ways to launch business ventures in new regions. While Afghanistan is a high risk zone with negative trade balance, weak currency & infrastructure, extreme security conditions and cultural di-versity, it is estimated to possess substantial reserves of oil and gas that may shake up Central Asia’s increasingly competitive energy contest.
The proposed framework is a step by step model which by using core competency check, decision metrics, balance scorecard models, cost equations, intelligent forecasting tools and several critical domains of knowledge management, makes sure that the organization takes right decisions regarding the manner of activities strategizing and execution, in mesh of diversified challenges. The SCOR model is integrated with processes that identify potential risk elements throughout the supply chain in Afghanistan, taken as base cases, and defines metrics to assess the potential impact of these risk elements. The framework thus developed has been used to build powerful simulation models that integrate discrete event simulation and spreadsheets. The simulation models are hierarchical and use sub models that capture activities specific to supply chains and breaks it down into further sub levels to evaluate the supply chain performance.
The model enables full leverage of capital investment, creation of a supply chain road map, alignment of business functions, reducing complexities in distribution network, and an average of two to six times return on investment. By maximizing predictability and controlling risk through excellence and efficiency in the upstream supply chain for Oil & Gas, this model provides an excellent tool for intelligent integrated management for any region that has similar mesh of chal-lenges as faced in Afghanistan.
ESP "Smart Flow" Integrates Quality and Control Data for Diagnostics and Optimization in Real Time (Part of KwIDF Project)
Authors: Al-Jasmi A.. , Nasr H.. , Goel H. K. , Moricca G.. , Carvajal G. A. , Dhar J.. , Querales M.. , Villamizar M. A. , Cullick A. S. , Rodriguez J. A. , Velasquez G.. , Yong Z.. , Bermudez F.. , Khain J.. 
Published: June 2014

AbstractIntelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time.The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers in their daily well surveillance activities, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and work flows for its production operations, with the ultimate goal of improved asset performance.The project includes a smart flow to monitor, diagnose, and optimize wells that include electric submersible pumps (ESP); 50% of the wells in this field use ESPs. The ESP smart flow includes leading-edge technologies, such as: variable speed drive controller, subsurface equipment and sensors, advanced diagnostics based on artificial-intelligence agents, analysis of sensors signals, and automatically identifying ESP optimum operating conditions.Using a steady-state nodal-analysis model combined with an artificial intelligent technique, the ESP smart flow is designed to provide rapid diagnostics and optimization in real time, generating actions, such as decreasing and increasing the pump frequency and choke setting. The ultimate benefit is to detect the signals that foresee unexpected well downtime and predict ESP system pump failures. The paper describes the main functionalities of the ESP smart flow as a powerful optimization tool that is capable of providing an interactive monitoring system that can assist operations personnel in managing ESP-operated wells.
Maximizing the Value of Real-Time Operations for Diagnostic and Optimization at the Right Time (KwIDF Project)
Authors: Al-Jasmi A.. , Goel H. K. , Al-Abbasi A.. , Nasr H.. , Velasquez G.. , Carvajal G. A. , Cullick A. S. , Rodriguez J. A. , Scott M.. 
Published: June 2014

Abstract
Although oilfield workflow automation processes have been implemented in the oil and gas industry during the last decade, the full impact and value of these technologies has yet to be realized. Tremendous potential still exists to implement new technology to maximize production, increase operational efficiency, and reduce down time/non-productive time. The Kuwait Integrated Digital Field project (KwIDF) is a novel intelligent digital oilfield operation for Kuwait Oil Company, the operator of the Sabriyah field in North Kuwait.
The project has successfully implemented improvements in field's operational efficiency by reducing the down time in artificial lift completion, increasing well productivity, and preventing water production. This paper provides an overview of the project from the perspective of field data acquisition, data analysis, model analysis, diagnostics, and operational optimization conducted in "right time" in the operator’s state-of-the-art collaboration center.
The automated workflow system orchestrates integrated solutions that not only provide monitoring in real time, but also enable diagnostics and optimization for individual wells. The automated workflow integration and orchestration conducted by the production team within a collaborative work environment covers the following aspects described in this paper: production surveillance (including alarming and alert events), well performance, production losses, artificial lift optimization, and key performance monitoring.
Impact of WITSML Data Surveillance on Shallow Casing Points
Authors: Khudiri Musab M , James John A , Gadri Waleed I , Bu Khamsin Ali H , Sanie Faisal S , Miyajan Ramzi A , Abbas Mazin Y , Kashif Muhammad , Jovin Tony 
Published: June 2014

Abstract
This paper describes the continued and increasing impact of the Wellsite Information Transfer Standard Markup Language (WITSML) on Drilling & Workover real time operations. Saudi Aramco— the biggest user and primary supporter of this industry standard — wants to share a new successful application of WITSML to their drilling activities.
Saudi Aramco Drilling & Exploration teams have commenced a project for the picking of Shallow Casing Points remotely from the Exploration and Petroleum Engineering Center (EXPEC). It has been observed from past experience that the sending of geologists to rig sites just to pick these Casing Points had always required significant manpower resources that could be better utilized for other frontline exploration jobs. In addition there is a sharply increased risk associated with the sending of these geologists to the remote field locations, since almost all of them are reached by travelling long distances by road.
The Rate of Penetration (ROP) is generally the only measured parameter that is available to assist with the picking of shallow Casing Points. By comparing this with the measured ROP, which is available from offset wells, known formation markers are identified and this allows the Casing Point depth to be chosen. In the project that is described by this Paper, exploration geologists were able to successfully identify the formation tops for most of the common surface formations in Saudi Arabia and thereby enable the picking of the Casing point in several pilot wells. This is an important milestone in the ongoing efforts of providing more cost--effective solutions for shallow sections, by using continuous real time data that is now being recorded from the spud of a well to the completion. This is being achieved remotely by using a real--time solution, called the Real Time Data Transmission and Viewer Service (RTDTVS), recently implemented at Saudi Aramco to collect the data from all of the drilling service contractors in a standardized format. This solution is referred to in this Paper subsequently as the "Aramcolink."
This Paper will address the important operational aspects that were implemented and demonstrate how this innovative technique has led to improved employee safety and operational cost savings.
Using Predictive Analytics for Asset Management: A Case Study of Early Warning Prediction Application
Authors: Ajayi A.. , Akinyemi O.. , Kurdi M.. 
Published: June 2014

Abstract
Producing assets face a pressing demand to minimize unplanned maintenance. The use of basic monitoring consoles such as DCS, are limited in scope, provide isolated views and reactive in nature. This paper presents a case study of using predictive analytics in an offshore oil production facility. The facility operations performance is constantly monitored by sensors sitting across an entire installation, measuring relevant information such as temperatures, pressure levels and flow rate at different time intervals.
Predictive analytics was applied to integrate all relevant data for proactive maintenance. The predictive model uses advanced analytics to estimate problem specific KPIs that is used to generate automated monitoring, alerting and event specific reports. These help to provide advance notice of impending failures and minimizing maintenance-related disruptions in operations.
The production facility consists of many integrated components as part of the production process. Continuous workings of these components are required to meet production targets. However, some critical components malfunction without warnings and, in the worst case, halt altogether resulting in short term production loss. The purpose of the predictive model was to use data from the entire process to find any measurable characteristics that may serve to warn that these detrimental situations are approaching. Moreover, the project also aimed at providing suitable diagnostics of the situation. In this technical paper, we described a successful application of an innovative statistical method for proactive asset management using predictive analytics.
The predictive models were able to successfully provide warnings of impending failures for the component of interest and identify root causes of such impending failures.
Integration of Operations, Safety and Security Assurance
Authors: Arango G.. , Flichy P.. , Jose S. J. 
Published: June 2014

Overview
There is an emerging consensus in our industry and in regulatory agencies about the need to improve our visibility of operations and to standardize as well as orchestrate the execution of operation processes to achieve integrated operations, safety and security assurance.
The complexity and risk of real-­-time exploration and production (E&P) operations is growing steadily along several axes: geology, operational environment, technology, distributed multi-­-discipline teams and multi-­-company teams. Operational performance, safety, and security assurance demand a high degree of situational awareness and real-­- time coordination.
At present, situational awareness is based on the visualization and monitoring of sensor data and, in some cases, of video feeds. Although data, video and audio travel through digital channels, they are rarely correlated or integrated. Decision makers are confronted with large amounts of data that are difficult to parse in real time and monitoring cannot be sustained over long periods of time because of distractions and fatigue. Furthermore, most decision making and coordination is based on people-­-to-­-people communication. These interactions are rarely captured, shared or integrated with other forms of data.
Other sectors involved in complex, high-­-stakes, real-­-time operations face similar conditions, for example, transportation, public safety or the military. They have learned to leverage digital technology to develop a "common picture of the operation" shared by all actors in a theater of operations. Sharing a common picture of the operation enables effective right-­-time response, efficient collaboration, and consistent decision making,
In this paper we outline:
The case for integration of operations, safety, and security assurance to create a common operations picture The capabilities of digital platforms to support a common picture of the operation How those capabilities can be leveraged in E&P operations.
Remote Communication, Collaboration and Cognition
Authors: Lomer Jocelyn 
Published: June 2014

AbstractAs operators search for hydrocarbons in increasingly remote and hazardous locations, so communications between the rig and head office become more challenging. The electronic media we use currently – email, telephone, electronic data transfer – all filter information, inhibiting collaborative cognition. This in turn can lead to misunderstanding of pivotal information, increasing risk to equipment, people, and indeed business value.In this paper we will describe how the use of fully collaborative technology, backed by wide ranging psychological research into how people communicate and collaborate both in a face-to-face setting and in a variety of remote situations, can be used to optimise collaboration in the most challenging situations, reducing risk, and significantly increasing rapid, accurate decision-making in the field.The Digital Oilfield is a reality, and the most significant change taking place is in the way in which people work in a collaborative environment. While there has been significant research into how different disciplines can work together, very little, if any, has gone into the way in which we collaborate remotely, and how that can be improved.Our research, extending the findings by the Universities of Cambridge, Kings College London, and Surrey, demonstrates conclusively that the provision of a remote collaborative environment that most closely resembles a face-to-face meeting – natural, intuitive, and able to handle complex tasks – improves knowledge sharing and interpretation, expands thought and memory processes, and in so doing, adds significant, measurable value to remote operations.
Data Mining Methodologies Enhance Probabilistic Well Forecasting
Authors: Holdaway Keith R. 
Published: June 2014

Abstract
Decline Curve Analysis (DCA) is one of the most common tools to predict oil and gas well performance and determine an estimated ultimate recovery based on historical production data. Probabilistic approaches have evolved to provide a measure of uncertainty in such estimates. However, engineers have held onto the belief that such quantification of uncertainty is largely subjective. Consequently there is an innate reluctance to adopt probabilistic methodologies owing to the assumption of prior knowledge of the relevant parameters and reservoir properties distributions.
The objective of this paper is to explicate the development of an improved probabilistic approach to estimate reserves and well performance based on historical production data. This methodology precludes the assumption of prior distributions by adopting a bootstrap workflow that is implemented to construct probabilistic estimates with specified confidence intervals from historical production data. It is a statistical approach to assess the uncertainty of estimates objectively, removing the subjective nature of prior assumptions.
We shall discuss an automated selection criteria workflow for time series and forecast periods resulting in more robust and accurate DCA. The methodology1 abides by a "more rigorous model-based bootstrap algorithm" that encapsulates the appropriate steps to preserve the inherent characteristics of a time series data set that show an overall decline trend.
The paper also explores other potential advanced analytical techniques to gather knowledge from historical upstream data so as to identify wells within an asset that require some remediation based on real-time performance observations. Cluster analysis could improve engineers’ convenience in analyzing wells by separating them into groups based on decline curve shapes (patterns) and other reservoir properties. We shall define some processes that implement soft computing techniques to aggregate the DCA results with disparate upstream data via a data driven methodology. Pattern recognition and classification are important steps that underpin data mining, and we shall explicate a suite of workflows to enable a well optimization solution.
Transforming Health, Safety and Environment Strategy and Culture Using Social Business
Authors: Weitner M.. , Hatler D.. 
Published: June 2014

Abstract
Over the past several years, the health, safety and environment (HSE) management paradigm has been shifting from a command-and-control, compliance-oriented model to a stakeholder management, market-based model. This new model is enabling organizations to differentiate themselves in the market through reduced operational risk, enhanced corporate brand and increased shareholder value. Organizations are finding it necessary to align HSE management processes with their overall business strategies and adopting a shared vision that represents the collective aspirations of the organization and its key stakeholders. This shift requires companies to dismantle what have previously been considered impervious internal organizational silos and open up conduits of communication between all stakeholders. Because this shift significantly impacts the culture of an organization, they can use social business tools, techniques and practices to ensure a more successful transformation and adoption to this new HSE paradigm.
Social business can help organizations better engage relevant resources and expertise, improve real-time communication and decision making, and enhance transparency and situational awareness. In this paper, the authors discuss how social business can be applied to the business of health, safety and environment management to support this paradigm shift from a liability managed program to an asset managed program. Such a shift will help companies evolve from a compliance reporting focus to a collaborative management process that encompasses their workers, partners, customers, shareholders and even the communities in which they operate.
Inflow Monitoring in Intelligent Wells using Distributed Acoustic Sensor
Authors: Xiao J.. , Farhadiroushan M.. , Clarke A.. , Khalifa Q.. , Mulhem A.. , Forero Reyes H.. , Parker T. R. , Shawash J.. , Milne H. C. 
Published: June 2014

Abstract
Intelligent well completions enable production and/or injection to be controlled and optimized across multilateral reservoir zones. The Distributed Acoustic sensor (DAS) is a promising technology for a number of wellbore monitoring applications such as distributed flow profiling. The DAS system can provide real-time monitoring across several zones equipped with Inflow Control Valves (ICVs) and Inflow Control Devices (ICDs) that are used to optimize the production and extend the life of the reservoir.
The new intelligent Distributed Acoustic Sensor (iDAS) uniquely allows the simultaneous recording of acoustic energy along many kilometers of optical fiber deployed along the wellbore. The system uses a novel digital optoelectronics detection technique that captures the amplitude and phase of the acoustic waves generated and propagated along the wellbore over a wide frequency (8mHz – 100kHz) range with a high dynamic range (&gt;120dB). A number of signal processing techniques have been developed for processing a large array of acoustic data that can be recorded along the wellbore for monitoring the inflow at different zones.
There are a number of intelligent well completions with fiber optic cable installations in Saudi Arabia. The DAS system described here can be retrofitted to the existing installations both with the multimode fibers, which were originally installed for the Distributed Temperature Sensor (DTS), and with single-mode fibers that are used for telemetry to point optical sensors.
The paper will report on the first survey results of the DAS measurement in an intelligent well that is equipped with ICVs. The results will show how the DAS system can be used to monitor the completion components such as by listening to the inflow noise across ICVs and ICDs and indicate the inflow contributions from different zones. The paper will present the first DAS survey using multimode fibers that was installed for the DTS measurements.