Asset-intensive companies face tighter maintenance budgets, stricter regulations and increased pressure to improve asset performance, whilst confronted with aging assets and workforce. Managing an asset with these challenges requires informed decision-making based on insight, knowledge and forecasting. Data is a powerful tool to achieve this goal. 'Internet of things' innovations have led to a rapid increase in the availability of technical and business data. A few years ago, techniques that were complex and expensive are now more affordable, accessible and increasingly important in order to compete in this world of rapid change. Field data is faster and immediately available for processing, while more relevant measurements and observations of similar or better quality are leading to more reliable information for decision-making.
The deployment and adoption of digital technologies across the oil and gas value chain could help scale the effect of the industry’s methane-reduction efforts, finds a new report by Environmental Defense Fund in collaboration with Accenture Strategy. While early adopters are using digitalization to gain a competitive advantage operationally, few of the world’s major oil and gas companies are using these solutions to reduce emissions of methane, a potent greenhouse gas. The paper, “Fueling a Digital Methane Future,” examines how innovations such as automated asset management, predictive maintenance, and the industrial internet of things (IIoT) can help prevent the annual loss of $34 billion a year in leaked, vented, and flared methane, the primary component of natural gas. According to the research, digital innovations have the potential to unlock more than $1.5 trillion in economic, environmental, and societal value across oil and gas operations. As more operators set methane targets or begin their methane management journey, digital innovations can support the realization of company goals.
In the last year, the oil and gas industry has continued to recover against a backdrop of steadily rising oil prices. Companies, however, continue to take a cautious approach in their investment decisions as they try to maximize value and minimize risk. The future remains uncertain, with oil-price fluctuations factored into decision-making. Further uncertainty prevails in an environment of increasing societal and investor accountability and a continually evolving energy landscape. Critically, any entity assessing project viability at any stage of the exploration-to-production life cycle must ensure an evergreen examination of the commercial likelihood on the basis of ongoing data analysis, refining development synergies coupled with strategic reframing as required.
Low Oil Prices and Global economy stagnation ring the bells to Operators to set plans for optimizing Operating Expenditures (OPEX) without compromising safety or operability of their Assets not to mention the need to extend the service life of such Assets. This applies for all operating assets whether these assets are Onshore, Offshore or Subsea. And there is no doubt that the need for optimization & efficient operation of Offshore and Subsea Assets are more and more crucial.
Asset Integrity Management (AIM) System is the Strategic Management tool designated to fulfill Operators’ targets where AIM is defined as the ability of an asset to perform its required function effectively and efficiently whilst preserving life and the environment through managing an integrative system.
Asset Integrity Management was believed to be for operating assets only however this is not valid anymore as AIM system shall be designed to cover all Asset's Lifecycle starting from conceptualization phase ending with decommissioning and abundant.
The fact that AIM activities are significantly diversified (as they cover all business aspects i.e. QHSE, Technical and Economics of the operating asset) makes it challenging to a have unified AIM Model or Framework. However, there are certain efforts nowadays by Key Industry Players to provide a model for AIM system but still under development.
The objective of the presented paper is to introduce a novel model/framework to assist establishing a rigor AIM system for different Oil & Gas plants whether it is Onshore, Offshore or Subsea.
The paper objective is achieved through presenting a wide literature review illustrating different codes and recommended practices efforts in establishing an Asset Integrity Management System and introduces a proposed model/framework for AIM system. The paper includes a detailed description for the proposed model/framework.
As a conclusion AIM is an essential strategic system that shall be designed, implemented and controlled for the sake of economic & safe operation however this system shall be set and planned form the early stage of projects. The proposed model/framework provides a novel and holistic approach that enables asset owners to plan & manage their assets efficiently and effectively.
Asset-intensive companies face tighter maintenance budgets, stricter regulations and increased pressure to improve asset performance, whilst confronted with aging assets and workforce. Managing an asset with these challenges requires informed decision-making based on insight, knowledge and forecasting. Data is a powerful tool to achieve this goal. 'Internet of things' innovations have led to a rapid increase in the availability of technical and business data. A few years ago, techniques that were complex and expensive are now more affordable, accessible and increasingly important in order to compete in this world of rapid change. Field data is faster and immediately available for processing, while more relevant measurements and observations of similar or better quality are leading to more reliable information for decision-making. The transition from data to information has been made possible through development in the usability of applications in the field of data science, and more advanced software and information systems are on the market for data analysis, diagnostics and simulation.
Cadei, Luca (Eni SpA Upstream and Technical Services) | Montini, Marco (Eni SpA Upstream and Technical Services) | Landi, Fabio (Eni SpA Upstream and Technical Services) | Porcelli, Francesco (Eni SpA Upstream and Technical Services) | Michetti, Vincenzo (Eni SpA Upstream and Technical Services) | Origgi, Matteo (The Boston Consulting Group) | Tonegutti, Marco (The Boston Consulting Group) | Duranton, Sylvain (The Boston Consulting Group GAMMA)
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to mitigate or avoid operational issues, maximizing the asset value, while maintaining the highest safety and environmental quality. This in-house developed tool is based on big data analytics techniques such as machine and deep learning algorithms.
The workflow developed allows predicting future events and the related influencing variables. This is done thanks to a powerful machine-learning algorithm specifically selected for the physical problem analyzed. The inputs come from a heterogeneous data-lake, composed by historical data, real-time series, maintenance reports, chemical analysis and operator experience. The workflow developed starts processing and enhancing this huge amount of data in order to train and validate the selected algorithm. Finally, the tool is fed with real-time data from the field, predicting potential events and prescribing possible actions to avoid problems that jeopardize the production and the integrity of the asset.
The tool has demonstrated the capability to predict in advance operational upsets occurring within the entire production system avoiding issues, maximizing the field availability. The case illustrated in this paper focuses the attention on the process section of an upstream oil field. In particular, process upsets of the sweetening unit, such as H2S out of specification, are analyzed since they affect not only the field production, but also the asset integrity and the environmental emissions. Several Big Data Analytics have been tested and presented in this paper, along with different methodologies of input-data pre-conditioning. Results related to the application of the tool on normal operations show a significant impact in terms of down-time reduction and production optimization. The possibility to have alerts and information a few hours in advance gives to the operator the ability to reach the asset operational target, which is not only related to the management of critical events but also to the achievement of the maximum level of production thanks to the definition of an optimal configuration of operating parameters. The tool highlights also the main parameters affecting the prediction suggesting corrective actions to prevent and mitigate risks and occurring critical events.
The innovative characteristics of the tool are the ability to take advantage of a huge amount of field data and to simulate complex phenomenon through mathematical-statistical methodologies, based on machine learning algorithms. Thanks to this innovative approach, it is possible to quickly predict possible hazardous events and consequently find the optimum asset configuration. This produces positive effects in the field short-term production optimization and the long-term maintenance strategies, maximizing its value and minimizing associated risks.
Asset-intensive companies face tighter maintenance budgets, stricter regulations and increased pressure to improve asset performance, whilst confronted with aging assets and workforce. Managing an asset with these challenges requires informed decision-making based on insight, knowledge and forecasting. Data is a powerful tool to achieve this goal. 'Internet of things' innovations have led to a rapid increase in the availability of technical and business data.
Petroleum reservoir simulation is the application of software designed to model fluid flow in petroleum reservoirs. I first encountered reservoir simulation while working on a project to store solar energy in an aquifer. During the first 3 years of my career, I performed a model study of a relatively small oil reservoir, reviewed the status of naturally fractured reservoir simulators, conducted an analysis of multidimensional numerical dispersion, evaluated the feasibility of developing geopressured/geothermal reservoirs, and compared the relative merits of different chemical flood processes. I thus found that a career in reservoir simulation would provide interesting challenges in a wide range of applications. The process of petroleum reservoir simulation is an aspect of reservoir management.
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