The Value of Real-time Geomagnetic Reference Data to the Oil and Gas Industry

Author(s):  
James Bowe ◽  
Simon McCulloch
2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Lukman Lawal ◽  
Onyekachi Ike ◽  
Raymond Olugbade ◽  
Fanen Gbuku ◽  
...  

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.


2020 ◽  
Vol 8 (5) ◽  
pp. 2582-2586

Automation and control systems are necessary throughout oil & gas industries, to production and processing plants, and distribution and retailing of petroleum products. Pipelines are the efficient mode of transportations of fuels for processing plants over long distances. At present Automation is achieved by using PLC’s that are communicated through SCADA. But it is complex and remote operation is not possible. With the introduction of IoT, the pipeline leak detection system is improved through real-time monitoring of the pipelines. Our Proposed system is designed to detect even small leakage that occurs within the pipeline. The implementation of IoT in oil and gas industries prevents accidents and to make quick decisions based on real-time data


2021 ◽  
Author(s):  
Evan Smith

Abstract Today's oil and gas industry is a global endeavor. With technological advances in data management and transfer, the ability for experienced engineers to receive, interpret, and make decisions from all over the globe in near real-time is not only achievable, but is becoming more desirable. Provoked by downturns and reduced personnel numbers, methods of increasing efficiency and cost reduction has gradually moved engineers away from the rig site, while still undertaking the same roles and responsibilities. This paper examines one case for an operator in the Caribbean. One major client drilling in the Caribbean was forced to explore reduced staffing options on one of its deep-water drilling rigs after flight cancellations, border closures, and isolation/quarantine procedures were implemented due to the COVID-19 pandemic. This made getting experienced data engineers and sample collection personnel to the rig site impossible. Two data engineers, two mud loggers, and two sample catchers are on the rig during normal operations, but with the above-mentioned challenges, only two mud loggers remained on site. The mudlogging service provider proposed intercompany collaboration with a region experienced in remote operational support, and a remote monitoring station was set up and manned with experienced data engineers to support real-time operations. A focal point between the remote engineers and the rig team was designated, and was responsible for communicating roles and responsibilities, linking the two teams. A robust communication protocol was established between the mudlogging crew, the remote personnel, the drill floor, and the company man which outlined specifics of which events would trigger communication between parties. Two intermediate hole sections were successfully drilled, without any interruption or delay. The remote engineers successfully participated in the rigs well control drills, calling directly to the rig when needed. During drilling, the experienced remote personnel were able to provide topic specific guidance to the less experienced engineers at the rig site, which accelerated their on-the-job training. This guidance encouraged and allowed for decreased reliance on the remote support over the course of drilling. The operator considered the implementation of the remote engineers a success and looked to implement additional remote resources from other service lines and providers. Development of additional remote support opportunities directly reduces risk and cost of personnel at the rig site throughout all aspects of the oil and gas industry. Reduction of personnel on site reduces overall exposure to the hazards associated with the rig site and would decrease the probability of incident. Recent improvements in technology and communication have made it possible for this to be a viable solution to de-manning the rig site in an evolving industry.


2021 ◽  
Vol 73 (05) ◽  
pp. 56-57
Author(s):  
Judy Feder

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 203461, “Digitalization in the Oil and Gas Industry—A Case Study of a Fully Smart Field in the United Arab Emirates,” by Muhammad Arif and Abdulla Mohammed Al Senani, ADNOC, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9–12 November. The paper has not been peer reviewed. One of the first oil fields in the UAE to be fully operated remotely is in the southeast region, 250 km from Abu Dhabi. The complete paper discusses the development and commissioning of the field, which is the first smart field for ADNOC Onshore. The designed and applied technology facilitated unmanned operation of the field from downhole to export. Introduction Oilfield digitalization encompasses gathering real-time and non-real-time data from wells, flow lines, manifolds, stations, and water injection facilities; analysis of the data using algorithms, flowcharts, plots, and reports; and user access to this data on user-friendly screens. This allows engineers to focus on interpretation of data vs. searching, organizing, and formatting the data. In the bigger picture, the data collected will lead to conclusions and set bases for important decisions for similar projects in the future, enabling a lesson-learning approach to design new oil fields. The accumulated theoretical and practical research results of smart-field implementation require analysis and synthesis to maintain perspective of the entire project. Both were applied in the Mender field, which is the subject of the complete paper. Problem Statement The Mender parent field has been producing since 2013 with minimal digitalization for wellheads. Wells are not fit-ted with remote sensors, and operators have been visiting the wells to collect data using analog gauges. Collected data are stored in computers or as hard copies. Some critical data is lost, which affects decision-making. The new Mender field is 50 km from the parent field and is in a sensitive area close to international borders. The field area is a wildlife reserve for various endangered animals. The nature of operations is highly critical because of concentrations of hydrogen sulfide (H2S) that could jeopardize employees’ health and safety.


2016 ◽  
Vol 11 (3) ◽  
pp. 376-394 ◽  
Author(s):  
Alkis Thrassou

Purpose Utilising a substantial volume of reliable international literature, information and positions – as well as many, less credible, local ones – the research analyses and interprets theoretical, secondary and primary data that are specific, relevant or peripheral to the emerging oil and gas industry of Cyprus. The purpose of this paper is to identify and investigate the forces and factors that affect the development of this very new industry; and to integrate them into a single provisional industry forces model. Design/methodology/approach This research is purely exploratory and is methodologically based on the review, comparison and interrelation of an extensive array of theoretical and secondary data works; which is reinforced and refined through an injection of primary qualitative work in the form of 20 expert and high-level interviews. Findings The research achieves the above-stated aim and further concludes with a schematic industry representation, allowing a comprehensive overview of the industry and additionally depicting some of the key interrelationship categorisations that constitute the key factors in decision-making at all levels. Originality/value The value of the research lies in its real-time approach to data gathering and analysis. The research aids in the understanding of the environment components, not simply as individual elements affecting their subject, but as a comprehensive system; demonstrating that it is this exact systemic understanding of the constituent elements that will support and facilitate the proper development of the industry. The research also bears global/generic importance as it provides a rare insight to the issues and complexities of a country having to first deal with the discovery of hydrocarbons in its economic zone.


Author(s):  
Svein Herman Nilsen ◽  
Massimiliano Russo ◽  
Guttorm Grytøyr

Over the last decades, the complexity and duration of offshore drilling operations have steadily increased. The size and weight of the risers and BOP stack has grown significantly. These factors have led to an increase in fatigue loads imposed on the wellhead structures during drilling and completion operations. Wellhead fatigue might ultimately lead to loss of well structural integrity and pressure containment and therefore safe and reliable drilling of subsea wellheads has gained high priority in the global oil and gas industry. This paper presents two of the most complex real time instrumentation campaigns for drilling operations. Analyses of a connected drilling riser system including the well structure are complex and involve several engineering disciplines. In addition, there are many unknowns going into the equations when accumulated fatigue damage of the wellhead is estimated. Therefore, assumptions need to be made, very often on the conservative side. A typical example are the global drilling riser analyses where the environmental conditions, actual rig motion and riser / BOP behavior are uncertain. With the duplex scope of accurately documenting the wellhead fatigue status during drilling operations and of achieving a better understanding of the actual risk level of wellhead fatigue, Statoil decided to start a very comprehensive monitoring campaign. Two MODU representing very different generations of rigs in terms of weights and types of equipment were instrumented from topside to BOP connector. Strain gauges were installed around the BOP connector as close as possible to the wellhead in order to capture wellhead response as accurately as possible. Due to the large number of sensors, high accuracy requirement and high sampling frequency of data to be shown live, a cabled solution was selected vs remote battery operated sensors transmitting via acoustic. Double set of cables, sensors and topside equipment were installed in order to make the instrumentation system fully redundant and suited for permanent installation. All data were additionally made available real time onshore to allow the full overview of the operation. To author’s knowledge, these two instrumentation systems are the most comprehensive and complex of this type installed on a drilling riser as of today. The first of the two system was installed approximately three years ago and it is still in operation. This paper describes the instrumentation systems installed and gives an extract of the quality data extracted and already used in already published studies [1, 2, 3].


Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.


2020 ◽  
Vol 8 (7) ◽  
pp. 532
Author(s):  
Giuseppa Ancione ◽  
Nicola Paltrinieri ◽  
Maria Francesca Milazzo

The oil and gas sector is one of the most dangerous and stringent workplaces, due to the hazardousness of materials involved as well as the critical tasks that workers have to perform. Cranes are widely used in this sector for several activities. A wrong load lifting or handling often is due to a limited visibility of working area and could bring to severe accidental scenarios, for this reason safety of these operations becomes of paramount importance. The use of safety devices, that provide an augmented vision to the crane-operator, is essential to avoid potential accidents, moreover risk analysis could benefit from the acquisition of real time information about the process. This work aims to extrapolate and adapt dynamic risk assessment concepts for crane-related operations of a typical oil and gas industry by means of the support of safety devices. To achieve this objective, a set of risk indicators, reporting continuous information about the operations that are carried out, will be defined; successively, a technique of aggregation of these indicators will also be applied with the aim to update the frequency of critical events by a proper Risk Metric Reduction Factor that accounts for the effect of the use of safety barriers.


Sign in / Sign up

Export Citation Format

Share Document