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Veros Systems took top honors at a competition involving energy startups. How can AI systems incorporate processes mimicking the slower logic- and causality-based reasoning patterns of the left brain? The technical directors’ special session at SPE’s Annual Technical Conference and Exhibition presented challenges on the E&P frontier in an industry grinding through a period of deep cost reductions. R&D may be the key to the survival of companies as the new economics of the industry take hold. BHGE shared its plans for the integration of its services, products, and digital platforms for upstream to downstream applications.
Just 3 years after its landmark merger, GE has announced that in the next 3 years it plans to sell of all its remaining shares in Baker Hughes. Baker Hughes is still a GE company, but it has partnered with a second company for artificial intelligence expertise, C3.ai. The deal is expected to speed the integration of AI into oilfield operations by the company which also markets GE’s device analytics platform, Predix. An assortment of sustainability initiatives shows how the oil and gas industry, leveraging its reach, diversity, and resources, is going well beyond just supplying energy to impact the world for the better. The firm hopes to remedy the cost-, labor-, and time-intensive process of executing offshore projects through deployment of “Subsea Connect,” which it says can cut project development costs by 30%.
Marathon Oil says its shale fields are producing more oil and gas with less hands-on work from company personnel thanks to a growing arsenal of digital technologies and workflows. BHGE shared its plans for the integration of its services, products, and digital platforms for upstream to downstream applications. Is Industry Ready for Brownfields’ Prime Time? The case for focusing on boosting recovery from older fields in a depressed drilling climate is compelling. At a breakfast session during IHS CERAWeek on squeezing more oil from brownfields in a low oil price environment, panelists discussed today’s improved field recovery capabilities.
A new open innovation studio aims to use crowdsourcing to redefine the future of oil and gas exploration. In tectonically influenced regions, potential hydrocarbon traps are subject to complex states of stress. This paper presents a coupled 3D fluid-flow and geomechanics simulator developed to model induced seismicity resulting from wastewater injection. Knowing which horizon crude oil flows from and in what proportions has been a major challenge for shale producers. Increasingly, they are turning to new technology to find the answer.
Fu, Jin (CNPC Engineering Technologies R&D Company Ltd.) | Wang, Xi (CNPC Engineering Technologies R&D Company Ltd.) | Yang, Guobin (CNPC Engineering Technologies R&D Company Ltd.) | Zhang, Shunyuan (CNPC Engineering Technologies R&D Company Ltd.) | Liu, Bingshan (CNPC Engineering Technologies R&D Company Ltd.) | Chen, Chen (CNPC Engineering Technologies R&D Company Ltd.)
High water cut is a serious problem for mature oilfields in China. Water injection paths are intersected with each other, injectors and producers are distributed in a more and more complicated pattern, while low-productivity layers are more focused. Conventional zonal water injection technologies require repetitive operation with wirelines and cables, causing extensive tests and low efficiency. However, an intelligent zonal water injection string consisting of several preset cable packers, water injection pressure gauges, formation pressure gauges and downhole flow meters has simply optimized water injection parameters and efficiently developed all reservoirs in some China's mature oilfields.
Horizontal drilling and extended reach well drilling technologies are developing by high speed, we are faced by more critical borehole conditions, which has brought more challenges to water adsorption testing of horizontal intervals and deployment of zonal water injection instruments. Compared with vertical wells, the water adsorption test and string running are more challenging for horizontal wells, in which we are faced by many a problem during zonal water injection, such as competitive slack off and tight pull, excessive or inadequate water injection, complicated operation process, etc.
There are several common challenges arising from zonal water injection, such as tight pull, slack off and repetitive operation. A full set of wireless intelligent water injection string for horizontal wells has been proposed. Based on pressure pulse water distribution technique, the water injection string is eligible for multi-stage adjustment, so it is possible to finalize testing, adjusting, injection, measurement, downhole data collection as well as automatic error correction in one run during water injection. A dedicated trial shows that integration of remote wireless control and artificial automation may is applicable, as packers are set securely and released easily, in order to adjust opening of each water injection nozzle on the ground. Therefore, this completion and water zonal water injection string enables precise water injection by means of data remote control. The first trial was accomplished in Ghawar Oilfield, Saudi Arabia, showing acceptable results.
Integration of remote wireless control and artificial automation requires a wireless water injection string that combines testing, adjusting, injection, measuring and data collection in one trip, providing us with adequate downhole data. Thus, the complete water injection process for each zone is precisely monitored and controlled on the ground.
Hafez, Hafez (ADNOC Upstream) | Saputelli, Luigi (ADNOC Upstream) | Mata, Carlos (ADNOC Upstream) | Mogensen, Kristian (ADNOC Upstream) | Di Sarria, Andrea (ADNOC Upstream) | Singh, Nicholas (ADNOC Upstream) | Mohan, Richard (ADNOC Upstream) | Escorcia, Alvaro (Frontender) | Pires, Joshua (Halliburton) | Asarpota, Jyotsna (Halliburton) | Ge, Haoyou (Halliburton) | Hernandez, Cristina (Halliburton) | Bansal, Yogesh (Halliburton) | Rodriguez, Jose (Halliburton)
Implementing large-scale projects within a company are challenging tasks and often provide a good learning curve that can be beneficial to understand the complexity of the work involved. An integrated subsurface to surface asset modeling solution was implemented at the country level to automate production capacity planning while optimizing shortfall and opportunity identification (
Several structured business processes support the developed system; it orchestrates the analytical processes followed by the corresponding approval system. A robust data management process was implemented and backed with a business process that includes more than 150 configurable exception rules. Besides, the developed solution leverages the rigor of the first principle and data-driven models to provide a desired and stable outcome ranging from potential evaluation, quota definition, capacity management, business plan validation, and other business processes. The developed solution can isolate wells, sectors, reservoirs, and/or fields for further evaluation. Given the challenge of balancing market demand with profits and subsurface deliverability, a time-efficient, balanced, and integrated solution is expected to provide an edge to an organization in this competitive environment.
The Integrated Capacity Model (ICM) system has already been utilized for capacity and deliverability of 2019 and 2020 ADNOC business plans demonstrating 99% agreement with field capacity tests. The system shown +3% profit gains through various production optimization scenarios, while recommending which assets, fields, and/or reservoirs can be targeted to achieve those targets.
Developing and implementing the solution at such a large scale surfaced various challenges at organizational, infrastructure, and solutions/workflows. This paper discusses those challenges and the ‘lessons’ learned during the implementation of this solution. Various value-added use cases are presented.
The objective of the present work is to streamline the analysis of Lease Operating Statements (LOS) with advanced learning paradigms from artificial intelligence (AI). The proposed approach aims at the: (a) consolidation of disparate expenses data; (b) timely expense assessment at field, pad or well level; (c) prevention and quick identification of negative cash flow trends (d) robust LOS predictions; and (e) optimal budget planning under uncertainty. To achieve this objective, a LOS acceleration platform was developed for the automatic integration of volume estimation with Lease Operating Expenses (LOE). Historical data from production volumes, revenues, price differentials, LOEs, marketing and transportation costs, taxes, CAPEX, P&A costs and others considered to strengthen the accuracy of individual expense categories and the overall LOS predictive model. The predictive model consists of a combined blend of analytical and machine learning models that allows to reliably forecast trends and proactively detect anomalies that may be negatively affecting the operational cash flow. Intuitive and portable visualizations allow a quick interpretation and communication of results among engineers and managers. The implemented platform fills a gap between traditional LOS analysis and preemptive expense planning involving many wells and expense categories that are hard to track daily. It is shown that the proposed approach can lead to savings of the order of 30% in incurred expenses.
Introductions, course layout PRMS 2018 key principles and how it works. PRMS matrix, risk and uncertainty, Why are reserves important? Proved developed producing (PDP), proved developed non producing (PDNP), Split conditions, Key issues and common mistakes. Exercises/examples PRMS Determination of project commerciality and how they can significantly impact reserves (contractual and fiscal issues, PSCs terms and conditions, project economics, costs models and how costs and other assumptions (e.g. Country and company track record and impact on reported reserves, Business Plan and company planning.