The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Mak, W. J. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Aziz, M. L. A. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Hamid, M. R. (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia) | Hashim, M. M. H. Meor (PETRONAS Carigali Sdn Bhd, Kuala Lumpur, Malaysia)
Abstract Technical engineering applications have long been an important tool for engineers in the well design process. The nature of being on-prem in individual devices has posed several issues, where lengthy installation processes often lead to a large amount of time consumption and disrupts the engineers’ work efficiency. As the industry moves into the era of digitalization, the Wells on Cloud solution is introduced. The migration of current on-prem applications into a SaaS Cloud-based solution aims to mitigate the issues encountered by users via the elimination of installation processes and automatic push updates. The WoC solution aligns with Wells Digital Roadmap to act as a central hub and a single source of truth while focusing on three key principles. It aspires to cater to solutions from different vendors, capitalize on established Cloud-based infrastructure to host the solution platform and facilitate data transparency. Being a Cloud-based solution, all updates can be implemented and pushed to the user's devices, and users can access their data with a device anytime and anywhere. Direct links to the Open-End Community are incorporated into the solution. Moving on, the WoC's security system consists of active detection of anomalies, protecting against these issues, and comprehensive strategies to respond to any potential breakdown. To measure the value creation resulting from this initiative, the quantitative process cycle efficiency and qualitative feedback survey are conducted. PCE improvements demonstrated the timeline improvement brought by this solution, along with the positive comments shared by heavy users of these applications. The successful deployment of the Wells on Cloud solution has equipped engineers with the tools to unlock the full capability of technical engineering applications right at their fingertips. It also opened the avenues for business agility and scalability to match business requirements, reducing the Total Cost of Ownership and leading to savings. This solution sets the path for Cloud-based working and drives the industry to move towards digitally enabled businesses.
This new webinar series provides comprehensive information about the Digital Oilfield (DO), from it's history to current applications. This package gives you access to all three webinars in the series or you can click to register for each individually. Each webinar is a 1-hour presentation followed by a 30-minute Q&A session. This webinar is categorized under the Data Science and Engineering Analytics discipline. Session I - What Have we Learned in the First 15 Years of Digital Oilfield?
This four-part webinar series will cover the reserves reporting regulations of the U.S. Securities & Exchange Commission. Each webinar is a 1-hour presentation followed by a live/recorded 30-minute Q&A session. This webinar is categorized under the Reservoir discipline. This module will review the major SEC definitions and will identify major contrasts with PRMS. This module will summarize major SEC reporting requirements.
This three-part webinar series is focused on introducing the basics of geomechanics and geomechanics services to a wide audience - from service approvers (management), to engineers and scientists who might be called upon to support and evaluate the value of geomechanics efforts but who do not have specific training in geomechanics. While presented as a three-part series, the webinars are laid out in a fashion from more general to more technical.
Daireaux, Benoit (NORCE) | Brackel, Hans-Uwe (Baker Hughes Company) | Ewald, Robert (NORCE) | Markussen, Petter (Prediktor AS) | Johansen, Maria (Sekal) | Parak, Mahdi (Halliburton) | Yadav, Ghanshyam (Halliburton) | Ismayilov, Anar (TotalEnergies)
Abstract Objectives/Scope Drilling operations rely on the collaboration of many participants, and the efficiency of this collaboration depends on timely exchange of information. The complexity and variability of this information make it difficult to achieve interoperability between the involved systems. Recent industry efforts aim at facilitating the many aspects of interoperability. A central element is semantic interoperability: the ability to correctly interpret the real-time signals available on the rig. This contribution presents an implementation of semantic interoperability using OPC UA technology. It translates the principles developed through joint industry efforts into actual drilling operations. Methods, Procedures, Process The process used the steps of characterizing the drilling real-time data with semantic graphs, and then developing methods to transfer this characterization to an operational real-time environment. A semantic interoperability API (application programming interface) uses the semantic modelling capabilities of OPC UA. Its objectives are to facilitate the acquisition and identification of real-time signals (for data consumers) and their precise description (by data providers). The different components of the API reflect the diversity of scenarios one can expect to encounter on a rig: from WITS-like data streams with minimal semantics to fully characterized signals. The high-level interface makes use of semantical techniques, such as reasoning, to enable advanced features like validation or graph queries. Results, Observations, Conclusions The implementation phase resulted in a series of open-source solutions that cover all the stages of semantic interoperability. The server part integrates real-time sources and exposes their semantics. Data providers can use dedicated applications to accurately describe their own data, while data consumers have access to both predefined mechanisms and to more advanced programming interfaces to identify and interpret the available signals. To facilitate the adoption of this technology, test applications are available that allow interested users to experiment and validate their own interfaces against realistic drilling data. Finally, demonstrations involving several participants took place. The paper discusses both the testing procedures, the results and insights gained. Novel/Additive Information The solutions described in this contribution build on newly developed interoperability strategies: they make on-going industry efforts available to the community via modern technologies, such as OPC UA, semantic modelling, or reasoning. Our hope is that the adoption of the developed technology should greatly facilitate the deployment of next generation drilling automation systems.
Abstract Digitalization of the drilling process has the potential to improve drilling data quality and consistency, providing support for drilling optimization, safety and efficiency. A significant barrier to realizing this potential is the data streams from the multitude of service companies, which changes almost daily, with variable definition of each of the real-time signals. This paper provides a solution to this problem: a method describing the semantics of real-time drilling signals in a computer readable format. For illustration, consider the calculation of mechanical specific energy (MSE) in drilling. It is possible to calculate a simple MSE signal in many ways, by using surface or downhole measurements, by applying corrections to the raw data, or by interpreting the equation in alternate ways. There is typically only a delivered value – the underlying details are lost. Semantic graphs bring transparency to the calculation by describing facts about drilling signals that are interpretable by computer systems. This semantic information encompasses details about signal measurement, and about signal calculation, correction, or conversion, yet all without exposing proprietary mathematical methods of calculation. It is possible, using semantic graphs, to assess the meaning and potential application of a signal, and whether or not the quality of the signal is suitable for its intended purpose. A semantic network relies on a vocabulary that defines a specific language dedicated to a particular topic, here drilling signals. The semantic network language is versatile: an existing language can describe new information and newly created signals. This provides a method meeting future needs without having to modify a standard constantly. In practice, each data provider exposes the meaning of its signals in the form of individual semantic networks. Merging these distinct semantic graphs provides a larger set of facts. This opens the possibility for synergies between independent data providers. For instance, applying logical rules infers new information. Since it is possible to query the semantic graph for signals that have certain properties, discovery of the most relevant signals at any time is feasible. By keeping track of modifications made to the semantic network during the drilling operation, it is also possible to post-analyze facts known about the available drilling signals, in an historic perspective. This is essential information for interpreting real-time data during offline data mining. This work is part of the D-WIS initiative (Drilling and Wells Interoperability Standards), a cross-industry workgroup providing solutions to facilitate interoperability of computer systems at the rig site and beyond. The D-WIS workgroup continues to develop the semantic vocabulary. The benefit of a computer interpretable description of the meaning of real-time signal is not limited to signals in real-time. Indeed, the method allows automatic data mining of historical data sets, facilitating the application of machine learning methods.
_ The use of satellite imaging to verify self-reporting of methane emissions using empirical data gathered in near-real time by artificial intelligence, could cost the fossil fuel industry dearly in fines under the new methane provisions of the US Inflation Reduction Act (IRA). Signed into law in August, the IRA requires the US Environmental Protection Agency (EPA) to adopt within 2 years methods to monitor and collect empirical data on methane emissions. The act introduces the federal government’s first-ever tax on greenhouse gas emissions, but does not specify preferred technologies. Satellite monitoring is assumed to be at the top of the list, according to geo-analytics company Kayrros. Private industry can, and likely will, play the same game, as it has access to the very technologies the government uses and can invest to acquire empirical data to identify problems companies might not even know they have. Once emissions are quantified at their source, industry can clean up supply chains and thus blunt the financial impact of penalties set to debut in 2024. Third-Party Audits: Good for ESG Optics but Missing the Bigger Issue In May, ExxonMobil announced it had become the first oil and gas company to earn an “A” grade certification under the independent MiQ standard for managing methane emissions from associated gas at its Poker Lake, New Mexico, facilities which the company said in 2021 accounted for more than 10% of its Permian Basin gas production. Responsible Energy Solutions, a Texas-based independent auditor in the field of Responsibly Sourced Gas (RSG), conducted the study. One month later, Chevron announced it had earned top marks from Denver-based Project Canary, an SaaS-based (software-as-a-service) data analytics company organized as a public benefit corporation which markets itself as the gold standard for independent RSG audits. It uses its own monitoring devices and does not run data through the client’s SCADA system. Gas producers are turning to third-party audits to differentiate their production streams as RSG to comply with the investment community’s ESG (environment, social, and corporate governance) requirements. In a study issued a year ago, the global research and business consultancy Wood Mackenzie referred to RSG certification as a “nascent” industry (audit companies tend to be innovation-driven data analytic startups) and noted that “certification processes exist (currently) only for upstream assets and there is no universal or industry standard.” “Certification is based on standards such as air emissions, water stewardship, land use, and community impacts,” Wood Mackenzie reported. “RSG differs from normally produced natural gas in that producers take extra steps to reduce their carbon footprint, mitigate emissions, and minimize environmental and social impacts.” RSG certification monitoring might collect empirical data—which is a start—but it is largely at pad level, ignoring the bigger and more ominous picture that can be assessed only from space. That bigger picture is what the EPA will turn to when it begins to levy its methane tax in 2024 and it is why the industry is starting to look “up” as well. Drone surveys are widening the view. A paper presented at the 2021 SPE Annual Technical Conference and Exhibition (ATCE) detailed how BP conducted the first methane emissions survey of an offshore facility with a miniature methane spectrometer onboard a fixed-wing unmanned aerial vehicle (SPE 206181).
This four-part webinar series will review fundamental computer science and programming concepts in the context of writing Visual Basic for Applications (VBA) "macros" to automate Microsoft Excel. We'll build simple automated tools for common oil & gas tasks while covering algorithms, data structures, program design, and debugging. Along the way we'll explore the Excel automation API, discuss the limitations of the Excel/VBA environment, and discover some topics for future self-directed learning. When you register you are automatically signed up for all four parts of this series! Each session runs 60 minutes.
The ability to detect interference between frac'ed and nearby wells is important to optimize completion strategies (most obviously well spacings and/or pumped volumes). And it is advantageous to be able to detect such interference in time to make operational decisions such as when to shut down or move to the next step in delivering a complex pumping schedule, to avoid over- or under-pumping. Interference between a frac'ed well and its neighbors can be detected using surface or downhole pressure changes induced in the neighbor well by direct connections between the pumped well and the neighbor well and/or by casing deformation induced by the offset frac; or by temperature changes or deformation associated with the interfering frac detected using fiber run into the neighbor wellbore or permanently installed as part of the completion. The benefits and drawbacks of these techniques are well known. These may include the requirement to run equipment into the well or to modify or add additional operational procedures.
MSA Safety is a leader in the field of Fixed Gas and Flame detection and with the launch of their next generation of gas detectors, they have enhanced the safety of workers that could be exposed to harmful toxic gases. At the same time, the new revolutionary technology enables their XCell sensors with TruCal capability to self-calibrate, extending manual gas calibrations for up to 2 years, which reduces the users cost of ownership. Mr. Cameron is responsible for the marketing at MSA The Safety Company of their fixed gas and flame detector portfolio, within the Middle East, Africa, India and Russian region. He has extensive experience in the F&G industry having worked for three fire and gas detector manufactures over a period of almost 20 years. Altogether his experience in the field of fire and safety spans almost 40 years, with the last 25 years based in the Middle East and working closely with the Oil, Gas and Petrochemical companies in the region. He has spoken at numerous events in the past and given various webinars on F&G topics.