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  • Passive Reservoir Monitoring by Use of Inter-well Tracers

    Contains 3 Component(s), Includes Credits Recorded On: 09/19/2018

    In this presentation, the speaker will briefly review the status of tracer technology, present recent improvements and highlight case studies that apply these methodologies.

    Click here for a sneak peek of the webinar.

    The use of tracers to monitor transport processes is a generic and versatile technique with a range of applications. In the subsurface domain, inter-well tracer testing has been used in more than 1000 injectors to monitor water and gas flooding worldwide the last 2-3 decades and has been used to map well-to-well communication and sweep and as a means to improve reservoir understanding. Recent tracer technology development, including new interpretation methodologies and access to a wide range of new tracers, has further strengthened this application of tracers. In this presentation, the status of tracer technology will be briefly reviewed. Recent improvements, including partitioning tracers will also be presented. Case studies that apply these methodologies will be highlighted. Operational challenges and pitfalls to avoid will also be discussed, as well as measures to be taken to assure reliability of the results.

    Dr. Olaf Huseby

    VP Technology & Interpretation, Restrack

    Dr. Huseby holds a PhD in Reservoir Physics from the University of Oslo (Norway). After PhD and Post-Doc studies in Poitiers and Paris on transport processes in discrete fracture networks, Olaf has 17 years’ research and consultancy experience in reservoir and tracer studies, including inter-well gas and water tracers, partitioning inter well tracers to assess oil saturation and single well tracer testing. He co-developed Restrack's interpretation schemes for inter-well and single well tracer data, including simulation as well as analytical techniques. He has published more than 40 scientific papers, and co-chaired and co-organized several SPE workshops, including SPE's 2014 and 2016 workshops on tracer technology that took place in Dubai and Kuwait respectively; and was co-chair of SPE tracer technology workshop that was held in March 2018 in Abu Dhabi.

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  • Meeting the Goals of the Paris Agreement: The Sky Scenario

    Contains 3 Component(s), Includes Credits Recorded On: 09/19/2018

    A new energy system is emerging. The Paris Agreement has sent a signal around the world: climate change is a serious issue that governments are determined to address. By 2070 there is the potential for a very different energy system to emerge.

    Click here for a sneak peek of the webinar.

    A new energy system is emerging. The Paris Agreement has sent a signal around the world: climate change is a serious issue that governments are determined to address. By 2070 there is the potential for a very different energy system to emerge.
     
    The Sky Scenario outlines what we believe to be a technologically, industrially, and economically possible route forward, consistent with limiting the global average temperature rise to well below 2°C from pre-industrial levels. It reveals the potential for an energy system to emerge that brings modern energy to all in the world, without delivering a climate legacy that society cannot readily adapt to. The changes are economy-wide, sector-specific, and amount to re-wiring the global economy in just 50 years.

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  • Creating Geologically Realistic Models Used for Reservoir Management

    Contains 3 Component(s), Includes Credits Recorded On: 09/18/2018

    Presented by Dave Stern

    To make sensible reservoir management decisions, it is necessary to predict future reservoir performance. This allows testing and optimization of reservoir management strategies before making large investments. When displacement mechanisms change or geologic description is different from current well locations, this prediction is usually done with reservoir simulation models. Because geologic features determine the connectivity and productivity of the reservoir, it is important to ensure that models realistically represent the reservoir description in order to provide plausible predictions. Challenges associated with constructing these models include:

    1. Uncertainty in the geologic description – measurements are sparse, and do not always resolve the relevant features.  It isn’t always known which features are relevant to reservoir performance.
    2. Geometry and stacking of geologic objects like channels and lobes are difficult to represent in cellular models
    3. Multiple descriptions may exist that are consistent with available data

    This presentation describes how reservoir models are used in making reservoir management decisions, and outlines a strategy for creating realistic reservoir models. Examples are provided of applying some elements of this strategy and examples of application to conventional reservoirs are reviewed.

    Dr. Dave Stern

    Retired from ExxonMobil Upstream Research Company

    Dr. Stern joined URC in 1984 with a PhD in Chemical Engineering from University of California at Berkeley and a BS in Chemical Engineering from MIT.  Research areas include experimental measurement of gas injection performance, development and use of simplified models for reservoir management, gridding and scale-up, and history matching. He led a team that developed tools for construction of simulation models from detailed geologic models, worked with software developers to implement that technology, and trained the rest of the corporation in its use.  Dave is the author of an SPE distinguished author paper on practical aspects of gridding and scale-up, describing learnings from that experience. Dave also led a team that developed tools and methods for history matching, with emphasis on preserving geologic realism during the history match process.  The team worked with software developers to implement the tools, and trained the rest of the corporation in their use. He finished his career at ExxonMobil as a reservoir engineering advisor to a large project that develops and maintains software for reservoir modeling and simulation. 

    Dave is a career-long member of SPE, and has served as session chair or discussion leader in SPE forums on gridding and scale-up, reservoir modeling for asset teams, and data analytics.

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  • Deploying Machine Learning on the Oilfield: From the Labs to the Edge

    Contains 3 Component(s), Includes Credits Recorded On: 09/13/2018

    Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment where the models are born.

    Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment where the models are born. 

    In this presentation, we will present the requirements of our oil and gas customers and explain how we were able to meet those requirements, such that we could deploy a new generation of analytics with a complete software engineering discipline and mentality around it by taking advantage of the Microsoft IoT Edge platform. This is currently a pilot project under way and, due to the engineering principles in place, we are able to complete a loop from the field to the lab and back again.

    Matthieu Boujonnier

    Analytics Application Architect, Schneider Electric

    Mr. Boujonnier is responsible for transforming data scientists' dreams (read: quick and dirty python code) into reality (read: o18n on the cloud and embedded devices). However, should he tweak their data and models them better, he will not stop himself.  Data science is a mindset, not a curriculum.

    Before joining Schneider Electric, Matthieu was designing solutions for major telecom and IT Services companies (Orange, Atos) and especially for Schlumberger where he was part of the architecture team working on remote management infrastructures for field services (drilling, fracturing, acidizing) and production optimization (ESP). Matthieu received his Master in Computer Sciences in France (Polytech Montpellier) and Canada (UQAM).

    Fahd Saghir

    Solution Manager, Upstream Oil & Gas, Schneider Electric

    In his latest role Fahd is responsible for outlining automation and software solutions for the Upstream Oil & Gas market and provide support to Schneider’s regional/international teams with technical input which help solve diverse Oil & Gas problems using the latest operation and information technologies.      

    An Electrical Engineering graduate from the University of Houston, who has 10+ years’ experience in Upstream/Midstream/Downstream Oil & Gas sector where he has been providing Smart Field solutions to National and International Oil  Companies. His focus has been in creating Optimisation Solutions based on the latest hardware and software automation technologies for the Exploration, Production and Operations verticals within the Oil & Gas sector.

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  • 4D Seismic Monitoring for Assessing Reservoir Heterogeneity and Its Impact on Hydraulic Fracture Stimulation

    Contains 3 Component(s), Includes Credits Recorded On: 09/12/2018

    This webinar will discuss the importance of understanding reservoir heterogeneity and its impact on the success of hydraulic fracturing in shale wells.

    Hydraulic fracture stimulation is an essential part of economically producing from unconventional resources such as shale and tight and stone reservoirs. Within the subsurface, reservoir heterogeneity and variability in elastic rock properties significantly control the response to hydraulic fracturing efficiency and effectiveness of hydraulic fracture stimulation. Understanding the link between the reservoir heterogeneity and the rock response to stimulation is essential to better develop and produce shale and tight reservoirs with efficient recovery. This webinar will discuss the importance of understanding reservoir heterogeneity and its impact on the success of hydraulic fracturing in shale wells. 

    A square-mile section within the Wattenberg Field, Colorado, USA, the Niobrara was hydraulically fracture stimulated and produced using 11 horizontal wells. These wells were used as a well spacing test to understand the effectiveness of hydraulic fracturing within the Niobrara and Codell formations. A three-dimensional geomechanical model was generated as an input to the hydraulic fracture simulation modeling.  4D time-lapse seismic and microseismic were also used to characterize the reservoir response to hydraulic fracturing and two years of production. The seismic monitoring was observed to be beneficial for the validation of hydraulic fracture simulation modeling results. Furthermore, the integrated analysis indicated that there is potential room for improvement and optimization needed for efficiently developing the Niobrara and Codell formations within the Wattenberg Field.

    Ahmed Al-Fataierge

    Geophysical Engineer, Saudi Aramco

    Mr. Al-Fataierge is an exploration geophysicist with 10 years of hands-on experience in the exploration and development of several conventional and unconventional reservoirs within Saudi Arabia and North America. Ahmed obtained his B.S. in Geophysics from the University of Houston in 2009. He also graduated with a M.S. in Geophysical Engineering and Minor in Petroleum Engineering from Colorado School of Mines in 2017.

    Ahmed has a passion for interdisciplinary integration of information for the better development of hydrocarbon resources. His technical background stretches beyond geology and geophysics through his work on multiple interdisciplinary studies. He is an accomplished explorationist successfully drilling several gas exploration wells within Saudi Arabia. Currently, he works within Saudi Aramco on the assessment and development of several unconventional resource plays within Saudi Arabia.

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  • A Conversation on the Interdisciplinary Nature of Geomechanics in the Oil and Gas Industry

    Contains 3 Component(s), Includes Credits Recorded On: 09/06/2018

    One of the most interesting aspects of applying oilfield geomechanics is arguably the amount of interdependency with other disciplines. While the pursuit of integrated, interdisciplinary approaches is increasingly common in our industry, this state of affairs can still be considered discretionary.

    One of the most interesting aspects of applying oilfield geomechanics is arguably the amount of interdependency with other disciplines. While the pursuit of integrated, interdisciplinary approaches is increasingly common in our industry, this state of affairs can still be considered discretionary. This means that technical teams can freely judge the value of inviting the contribution from other disciplines given a state of imperfect information. One example of such condition is being unable to decide on the need of involving experts from other disciplines: Is it a lack of reliable data feeding our physics models or a missing piece of physics (or knowledge) captured only by another discipline?

    Interestingly, such discretionary opportunities are very few in the world of geomechanics. Compared to many of the core subsurface and engineering disciplines in our industry, the application geomechanics is an essentially –and necessarily- integrated effort. This situation has interesting repercussions in modelling and communication strategies employed for geomechanics projects where the observations, measurements and results most likely belong to members of a different technical domain. While showing examples of the field applications of geomechanics, we will explore how geomechanical workflows have adapted to cater for the commonality of technically diverse stakeholders and how circumventing some of these challenges might find an audience in other disciplines, especially young professionals concerned with technical communication skills.

    Adrian Rodriguez-Herrera

    Numerical Geomechanics Expert

    Mr. Rodriguez-Herrera is a numerical geomechanics expert with background in petroleum engineering, numerical methods and software development. He started his career with a series of assignments in Schlumberger technology centers for heavy oil and reservoir geomechanics. Over the past 9 years, his experiences have comprised a blend of innovation, software development and international consulting projects. Spanning topics around reservoir engineering, drilling, well integrity and hydraulic fracturing, his focus lies in the development of methodologies and software applications that bridge the gap between operational or geophysical observations and the underlying geomechanical mechanisms that could explain them. He has consulted on the application of these topics for operators in over 20 countries, and led the development of several of the enabling software technologies. Since 2016, he leads the Geomechanics Centre of Excellence in London, Schlumberger’s global consultancy team for oilfield applications of numerical geomechanics.

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  • QRI Expert Hour -- Evaluate and Enhance Artificial Lift System Performance Through Data-Driven and Model-Based Technology

    Contains 2 Component(s) Recorded On: 09/06/2018

    Content for this webinar is provided by QRI. By registering, your contact information will be shared with the sponsor.

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    Improve and assess the performance of artificial lift system by employing a technology to implement smart analytics for increasing production, reserves and capital efficiency.

    Dr. Hamed Darabi

    Technology Execution Leader, QRI

    Dr. Darabi is the technology execution leader at Quantum Reservoir Impact (QRI). His current responsibility is to maintain key QRI technologies, expand QRI’s intellectual property, and ensure high-quality delivery of QRI technologies to clients. Since 2013, Dr. Darabi served as the team lead for multiple filed studies, and was involved in the development various QRI's proprietary products. He also worked on several giant fields in Middle East to implement QRI technologies and perform reservoir studies. Prior to QRI, Dr. Darabi worked as reservoir engineer at Occidental Oil & Gas Corporation and various companies in Middle East, since 2006. His experience spans reservoirs in California, Kuwait, Partitioned Zone, Iran, UAE, Mexico, and Iraq. Dr. Darabi received his Ph.D. in Petroleum Engineering from The University of Texas at Austin, where he extensively studied reservoir simulator development and mathematical modeling. For his dissertation, he developed a non-isothermal compositional simulator to model asphaltene precipitation, flocculation, and deposition in oil reservoirs and near wellbore. Moreover, he studied the condition of asphaltene precipitation in Asab field in UAE during CO2 Injection.

    Dr. Ehsan Davani

    Senior Production Engineer, QRI

    Dr. Davani is a senior production engineer at Quantum Reservoir Impact (QRI) with over 10 years of worldwide industry experience. He has an extensive knowledge in artificial lift optimization, integrated production modeling, digital oil field and asset management. He was involved in field development planning and artificial lift optimization projects in seven giant fields in Colombia and Peru. Prior to QRI, Dr. Davani worked in Chevron and EDG consulting engineers for eight years. He was leading a team to build and implement of an integrated production digital oilfield system for Chevron deep-water assets in the Gulf of Mexico, Angola, Nigeria, and Thailand.

    Dr. Davani is a registered Texas professional engineer (PE). He has (co)authored several technical papers and a book. He also serves as an associate editor in Journal of Natural Gas Science and Engineering (JNSGE) since 2013.

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    Content for this webinar is provided by QRI. By registering, your contact information will be shared with the sponsor.

  • Machine Learning And Data Analytics In Flow Assurance

    Contains 3 Component(s), Includes Credits Recorded On: 08/30/2018

    The democratization of machine learning and data analytics has thrown open a variety of new tools to the industry. A few examples will be shown in this presentation showing how these tools can be used ranging from reduction in study times to creating new methods of studying/troubleshooting difficult problems.

    Click here for a sneak peek of the webinar.

    The democratization of machine learning and data analytics has thrown open a variety of new tools to the industry. These tools create new opportunities in the flow assurance area in both design studies and in operations phase. A few examples will be shown in this presentation showing how these tools can be used ranging from reduction in study times to creating new methods of studying/troubleshooting difficult problems.

    Dr. Prabu Parthasarathy

    VP of Intelligent Operations, Wood

    Dr. Parthasarathy completed his PhD in Mechanical Engineering with a specialization in CFD application to multiphase flow from the University of Houston. He started at Wood in 2002 as a flow assurance engineer and has led flow assurance teams in various phases of design. As the VP of intelligent Ops within Wood, Prabu looks after a portfolio of software products and services that deal with operational issues in the upstream, downstream, manufacturing and other industries.

    SPE Webinars are FREE to members courtesy of the

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  • Killer Communication Skills

    Contains 3 Component(s), Includes Credits Recorded On: 08/28/2018

    Participants will reap the benefits of effective communication and learn how to motivate and persuade others without resorting to the traditional command and control approach.

    Through this webinar, participants gain the essential basics of powerful and effective communication. They will learn the pitfalls of communication and how to communicate clearly. Participants will reap the benefits of effective communication and learn how to motivate and persuade others without resorting to the traditional command and control approach.

    Brent Darnell

    Owner/Founder, Brent Darnell International

    Mr. Darnell has been teaching critical people skills and emotional intelligence to engineers since 2000. In 2012 he was awarded Engineering News Record’s top 25 newsmaker’s award for his record breaking program that “transforms Alpha males into service focused leaders”. His bestselling books, The People Profit Connection and the Tough Guy Survival Kit along with online courses are helping to transform the industry.

    SPE Webinars are FREE to members courtesy of the

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  • Embracing the Possible: Applying Cross-Transferable Data Analysis and A.I. Innovation from Other Industries

    Contains 2 Component(s), Includes Credits Recorded On: 08/14/2018

    This presentation will explore draw on relevant examples using data, predictive analytics, A.I., augmented intelligence systems from the company’s partnerships with oil and gas operators and service companies.

    This presentation will explore the following points, drawing on relevant examples using data, predictive analytics, A.I., augmented intelligence systems from the company’s partnerships with oil and gas operators and service companies: subsurface workflows, drilling ops management (precision, avoidance of stuck pipe), asset health monitoring and failure prediction, optimized P&A strategies, but also within aerospace: aircraft digital twins, ‘the Conscious Aircraft’, intelligent helicopter pilot decision-support, aircraft landing-gear failure prediction, transport: National-scale rail network monitoring and failure prediction. 

    • Accelerating innovation through applied learning from other sectors (aerospace, transport, banking services)
    • Bench-marking the oil and gas (energy) sector against the innovation curve of other industries
    • Examples of cross-transferable capability, intelligence and application
    • The shift from strategy to action and implementation
    • Fundamental change: avoiding siloed teams, siloed data and isolated programs
    • Removing the blinkers and improving visibility and collaboration

    Angela Mathis

    Chief Executive, ThinkTank Maths Limited

    Ms. Mathis, CEO and co-founder of ThinkTank Maths Limited (Edinburgh), providing mathematics, (predictive) data analysis and AI-based software solutions for ‘augmented intelligence’ decision-support to oil and gas, aeronautics, space, defence, health and banking services. Partnership projects include Smart Cities/Airports, ‘The Conscious Aircraft, drilling accuracy quality control and asset failure prediction.

    Angela joined the Board of the Scottish Oil Club in 2015. She is a Council member of ADS, UK trade body for Aerospace, Defence, Security and Space, and on the Market and Economics Advisory Group, UK Aerospace Technology Institute (ATI).

    Her professional history is in international technology commercialization at I.C.I., Nobel’s Explosives (Scotland), Polyurethanes (Belgium), Lucent Avaya (EMEA). She launched the Iomega Zip drive across Europe achieving $600m revenue in 2 years, and led the commercial expansion of PSINet Europe.

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