Mawlad, Arwa Ahmed (ADNOC Onshore) | Mohand, Richard (ADNOC) | Agnihotri, Praveen (ADNOC Onshore) | Pamungkas, Setiyo (ADNOC) | Omobude, Osemoahu (ADNOC) | Mustapha, Hussein (Schlumberger) | Freeman, Steve (Schlumberger) | Ghorayeb, Kassem (American University of Beirut) | Razouki, Ali (Schlumberger)
Challenges associated with volatile oil and gas prices and an enhanced emphasis on a cleaner energy world are pushing the oil and gas industry to re-consider its fundamental existing business-models and establish a long-term, more sustainable vision for the future. That vision needs to be more competitive, innovative, sustainable and profitable. To move along that path the oil and gas industry must proactively embrace the 4th Industrial Revolution (oil and gas 4.0) across every part of its business. This will help to overcome time constraints in the understanding and utilization of the terabytes of data that have been and are continuously being produced. There is a clear need to streamline and enhance the critical decision-making processes to deliver on key value drivers, reducing the cost per barrel, enabling greater efficiencies, enhanced sustainability and more predictable production.
Latest advances in software and hardware technologies enabled by virtually unlimited cloud compute and artificial intelligence (AI) capabilities are used to integrate the different petro-technical disciplines that feed into massive reservoir management programs. The presented work in this paper is the foundation of a future ADNOC digital reservoir management system that can power the business for the next several decades. In order to achieve that goal, we are integrating next generation data management systems, reservoir modeling workflows and AI assisted interpretation systems across all domains through the Intelligent Integrated Subsurface Modelling (IISM) program. The IISM is a multi-stage program, aimed at establishing a synergy between all domains including drilling, petrophysics, geology, geophysics, fluid modeling and reservoir engineering. A continuous feedback loop helps identify and deliver optimum solutions across the entire reservoir characterization and management workflow. The intent is to dramatically reduce the turnaround time, improve accuracy and understanding of the reservoir for better and more timely reservoir management decisions. This would ultimately make the management of the resources more efficient, agile and sustainable.
Data-driven machine learning (ML) workflows are currently being built across numerous petro-technical domains to enable quicker data processing, interpretation and insights from both structured and unstructured data. Automated quality controls and cross domain integration are integral to the system. This would ensure a better performance and deliver improvements in safety, efficiency and economics. This paper highlights how applying artificial intelligence, automation and cloud computing to complex reservoir management processes can transform a traditionally slow and disconnected set of processes into a near real time, fully integrated, workflow that can optimize efficiency, safety, performance and drive long term sustainability of the resource.
Labrousse, Sébastien (Schlumberger) | Guner, Hakan (Equinor ASA) | Kauffmann, Carlos (Equinor ASA) | Caycedo, Alberto (Schlumberger) | Opsahl, Jørn (Tomax AS) | Atallah, Rawad (WWT International Engineering Services) | Hatleseth, Tore Andreas (KCA Deutag Drilling Norge AS) | Moldekleiv, Rune (Schlumberger) | Nokland, Magnar (Schlumberger) | Andreassen, Ørjan (Schlumberger) | Nyborg, Benjamin (Schlumberger)
The purpose of the paper is to present how an integrated solution was designed to turn a challenging 6-in. section into a successful 6-in. production sidetrack in Norway. A threatening casing wear issue caused by the combination of slow progress and localized dogleg was addressed successfully with a complete redesign of the drilling system.
A 6-in. pilot section suffered slow progress due to low rate of penetration and tool failures. Significant amount of metal swarf was recovered while drilling. A casing wear log quantified the wear in the 9 7/8-in. casing, and this led to questioning the feasibility of the planned 6-in. production sidetrack. Operator, rig contractor and integrated services provider worked together to find a solution.
First, a detailed study of the wear was performed. A wear log was run, and the casing wear was quantified. Casing wear simulations were then calibrated based on wear logs and it appeared feasible to drill the 6-in. sidetrack if a minimum rate of penetration and a maximum number of revolutions were respected.
Second, the drilling system was optimized to ensure faster progress. This was done thanks to the learnings from the pilot section. The mud system was changed, and a lower density was used to increase the rate of penetration. The drillbit was optimized based on the limited wear seen in the bits used in the pilot section. As it was more aggressive, the perceived risk of downhole tool failure was mitigated with the use of an anti-stall tool.
Finally, to reduce the incremental wear from the sidetrack operation, casing protectors and lubricants were run. Also, the planned drillpipe was changed to a lighter drillpipe to reduce the sideforces.
The new system resulted in a successful drilling and section TD was reached ahead of the estimated perfect time.
With this paper we provide a detailed example of how a casing wear issue was addressed. The drivers we extract from this case are useful for the planning of future operations, especially in extended-reach wells.
In this paper we will set out how we maximise the value created by the digital revolution through the use Systems Thinking and Agile techniques to establish a FEL 0-1 Digital Twin, we will then describe how we use a BIM approach to evolve this Digital Twin through the project lifecycle; fostering collaboration, breaking down siloes, creating and protecting value as we do so. Two case studies, one an offshore gas compression project and the other a normally unmanned wellhead installation, will be presented to demonstrate the application and effectiveness of this approach.
Although the headline that gets the clicks is “AI is taking our jobs,” the current reality is that “Automation is replacing some of our tasks.” This article gives a succinct overview of artificial intelligence, its emerging opportunities, prospects, and challenges, and concludes with recommendations to accelerate the admission of AI into workflows. In addition to affecting how young professionals will do their jobs in the future, the arrival of AI can have an effect on how they find jobs in the present. How can YPs prepare for the increasingly automated hiring processes? The AI revolution in the market for consumer goods by companies like Amazon and Alibaba led to significant changes in the market dynamics.
Diverting-spinner flowmeters are the most accurate of the spinner devices when low total rates and multiphase flows occur. The stream is diverted through the tool's barrel, thereby raising the velocity of flow and increasing the sensitivity to the point that diverting spinners can detect rates as low as 10 to 15 B/D. Furthermore, a flow of 100 B/D passes through the barrel at 34 ft/min, which is sufficient to start the homogenization of the flow, which eventually eliminates phase influence. In casing, a rate of 2,000 B/D is needed to have the same effect around a continuous spinner. Another benefit to multiphase-flow application is that the tool can be calibrated directly for such flow.
This paper describes the step performance improvement of a 16’’ section in a UAE offshore application. Through close collaboration between directional drilling services, drill bits and operator, a 44% improvement in the section ROP from the field average was achieved. The novel solutions of bit and drive system and drilling practices which allowed for this improvement will be detailed in this paper.
A key contributor to this achievement was utilizing a new hybrid bit technology platform which incorporates the dual cutting mechanisms of both polycrystalline Diamond Compact (PDC) and tricone bits. This allows for more efficient drilling through bringing together the improved ROP performance of a PDC bit and the reduced torque fluctuations of a tricone bit. Where initially drilled by tricone bits, the application posed potential for performance improvement, which was to be explored with this bit and BHA solution. Continuous optimization of drilling parameters was essential to minimizing the vibrations and improving the overall drilling efficiency.
As a result of the proposed bit and drive system solution and the considerations in execution, the 4900ft long section was drilled in a shoe to shoe run, from surface to Fiqa formation. The unique compatibility between the bit design and the drilling motor allowed for exceeding the intended performance KPI set by the operator. The directional objectives were met as the well was precisely steered between offset wells with critical proximity, where the minimum ellipse separation expected was as low as 1.2ft. The new bit design was seen to not compromise the directional steerability as may be of concern with a PDC bit with a motor. The ROP achieved on the section was a 44% improvement from the field average. Given this being the first run from this new hybrid bit platform, this application still holds potential for further improvements upon design changes based on learnings from the first run. The outcome ultimately would be to take the conventional tricone drilling of this 16’’ section to a complete other performance level.
However, analysis of large datasets and AI-driven insights have already enjoyed relatively broad and early adoption in exploration and production. For example, the “dry hole” rate (the rate at which a new well fails to achieve commercially viable production) has fallen from about 40% in the 1960s to around 10% today. This improvement in efficiency, leading to a significant reduction in exploration costs, is largely the result of improved analytics on seismic, satellite, and other data sources extracted from the basin. Similarly, the productivity of wells has increased as new analytical tools guide improving completion technologies, including hydraulic fracturing, horizontal drilling, and artificial lift technologies. In contrast, AI and big data analytics have not enjoyed nearly the same adoption in the midstream sector, even though there are several significant opportunities.
The Fast Drill Process has become a well-known work flow to identify hole-making limiters and mitigate them through redesign to the economic limit of performance. To address operations that do not include drilling of rock, the operator launched a similar effort and work flow that focus on flat-time portions of well construction. This process is yielding significant savings globally and has been accomplished through planning, real-time recognition and response, and collaboration while improving safety performance continuously. In 2004, the operator started a pilot program to determine if drilling performance could be improved by analyzing and reacting to trends in mechanical specific energy (MSE). MSE is a performance measurement parameter that approximates the bit's drilling efficiency and is a function of weight on bit (WOB), surface torque, bit rotation per revolution, and rate of penetration (ROP) for a given hole size.
Although oil experienced an extraordinary price increase over the past 4 decades, a turning point has been reached where scarcity, uncertain supply, and high prices will be replaced by abundance, undisturbed availability, and suppressed price levels in the decades to come. In our new book, The Price of Oil, we conclude that the shale revolution will yield an increased output of oil in the world totaling nearly 20 million B/D by 2035. We also assert that a "conventional oil revolution"--the application of horizontal drilling and hydraulic fracturing to conventional oil formations in the world--will yield a further addition of almost 20 million B/D in the same period. This extra 40 million B/D is nearly twice as much as the global increase in oil production in the 20-year period from 1994 to 2014. As these new production revolutions develop and expand internationally, they are bound to have a strong price-depressing impact, either by preventing price rises from the levels observed in 2015 (the Brent spot price averaged USD 53/bbl), or by pushing prices back to these levels if an early upward reaction takes place.
The US may become the global leader in liquefied natural gas (LNG) exports, according to a new report by the International Energy Agency (IEA). To ascend to the top spot, US LNG shipments must surpass today's leading exporters, Australia and Qatar. In an announcement made on Thursday, the IEA called US LNG a "catalyst for change" and highlighted how production from tight shale gas fields is transforming the world's natural gas marketplace. The Paris-based organization also said the country's exports will help fuel economic growth in developing countries. "The US shale revolution shows no sign of running out of steam and its effects are now amplified by a second revolution of rising LNG supplies," said Fatih Birol, the executive director of the IEA.