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Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality. All three can lead to poor decisions regarding which work to undertake, what issues to focus on, and whether to forge ahead or walk away from a project. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders. Examples are provided including corporate, business unit and department case studies. This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets.
SPE, through its Energy4me programme, will present a free one-day energy education workshop for science teachers (grades 8–12). A variety of free instructional materials will be available to take back to the classroom. Educators will receive comprehensive, objective information about the scientific concepts of energy and its importance while discovering the world of oil and natural gas exploration and production. Energy4me is an energy educational public outreach programme that highlights how energy works in our everyday lives and promote information about career opportunities in petroleum engineering and the upstream professions. SPE’s Energy4me programme values the role teachers and energy professionals play in educating young people about the importance of energy.
Challenges In Drilling and Completion Of Extended Reach Drilling Wells with Landing Point Departure more than 10,000ft in Light/ Slim Casing Design. New Generation of HTHP Water Based Drilling Fluid Changing Conventional Drilling Fluids Solutions. Take Back Control of Your Capital Project with an EPC 4.0 Strategy Stratigraphical - Sedimentological Framework for the Thamama Group Development in the Western UAE Based on the Legacy Core Data: How the Key to the Future is Found in the Past. Ultra-deep Resistivity Technology as a Solution for Efficient Well Placement; Geosteering and Fluid Mapping to Reduce Reservoir Uncertainty and Eliminate Pilot Hole-first Time in Offshore Abu Dhabi, UAE. Performance Comparison of two different in-house built virtual metering systems for Production Back Allocation.
In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells. The authors present a new data-driven approach to estimate the injection rate in all noninstrumented wells in a large waterflooding operation accurately.
Measuring the flow of water, mud, and cuttings from a well is critical, and difficult. A new flowmeter design that promises to be both accurate and durable is one of three technologies featured in a JPT series on drilling measurement innovation. This paper describes a virtual metering tool that can monitor well performance and estimate production rates using real-time data and analytical models, integrating commercial software with an optimization algorithm that combines production and reservoir information. In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells. Australian technology developer MezurX is touting its newly introduced flow, density, and mud monitoring system as a significantly better alternative to the widely used Coriolis meter.
DNV GL and floating production, storage, and offloading (FPSO) vessel specialist Bluewater are undertaking a pilot project to use hybrid digital twin technology to predict and analyze fatigue in the hull of an FPSO in the North Sea. Akers BP said it will use lessons learned from the pilot and scale the remote-assist concept across its assets. The complete paper provides an approach using machine-learning and sequence-mining algorithms for predicting and classifying the next operation based on textual descriptions. The dynamic nature of unconventional-reservoir developments calls for the availability of fast and reliable history-matching methods for simulation models. In this paper, the authors apply an assisted-history-matching approach to a pair of wells in the Wolfcamp formation.
To drive progress in the field of data science, the authors propose 10 challenge areas for the research community to pursue. Because data science is broad, with methods drawing from computer science, statistics, and other disciplines, these challenge areas speak to the breadth of issues. Incorporating imagination into AI agents has long been an elusive goal of researchers in the space. Imagine AI programs that are able not only to learn new tasks but also to plan and reason about the future. Data scientists working with large data sets or in high-performance computational environments may find these programming languages essential to extracting data quickly and effortlessly.
The service companies plan to co-market an emerging well control system that can integrate with established managed-pressure-drilling components to enhance well construction safety and efficiency. The next step is to move toward optimization, then automation. An intelligent drilling optimization application performs as an adaptive autodriller. In the Marcellus Shale, ROP improved 61% and 39% and drilling performance, measured as hours on bottom, improved 25%. A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream.
High-fidelity 3D engineering simulations are valuable in making decisions, but they can be cost-prohibitive and require significant amounts of time to execute. The integration of deep-learning neural networks with computational fluid dynamics may help accelerate the simulation process. Reducing a separation system’s footprint while increasing separation efficiency is demonstrated in an Oklahoma field trial. Reliable separation is becoming an enabling technology to help develop remote location resources and more difficult applications, such as heavy oil, produced water, sand disposal, and back-produced fluids in enhanced oil recovery. This paper provides details of comprehensive computational-fluid-dynamics (CFD) -based studies performed to overcome the separation inefficiencies experienced in a large-scale three-phase separator.