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

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As rightly said, Liquid Liquid separation is often termed as a “Black Box” in the industry, as, accurately predicting BS&W and the efficiency of “continuous in dispersed” has always remained questionable . Whether engineering a new Coalescer vessel, or debottlenecking an existing separator, full knowledge and understanding of the basic principles involved are required and the design should be treated as a system design rather than an equipment design, as, neglecting the fundamental principle may result into inadequate performance. Often overlooked are the capabilities of properly selected and designed internals for the enhancement of simple gravity separation, location of coalescing equipment’s due to mechanical constraints, residence time as the length needs to as short as possible, velocities of continuous & dispersed phase as the diameters are fixed, potential of fouling due to presence of solid, inversion point etc etc. The presentation will cover basic design philosophies involved in properly designing a liquid liquid separation vessel along with some case studies.

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In this webinar I will show some examples of my drone acquisitions applied to geology, trusting that this will be a good opportunity to share ideas on the techniques and areas of application of this technology. In general, I use drones to generate digital models of the topography that can help understanding certain geological features in a more intuitive way.

I publish the models in Sketchfab, which I found to be a very effective platform for sharing 3D models. I complete them in different ways, by adding annotations, animations, integration with geological maps and sections, interpretation of geological surfaces, 3D printed scale models. The main application that I see for these models is to provide support to the work in the real field, either in preparation for an actual field trip, or as a means of sharing observations on a remote location. In one case though, I have used a model not just as a 3D display, but rather as a digital dataset to test geological concepts in a more rigorous way.

The areas of acquisition of my models are mostly from Spain although one model is from the Venezuelan Andes and another one is from the SE coast of Italy. Finally, another model is related to an archaeological topic.

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Saudi Aramco has been drilling horizontal and multilateral wells to develop gas fields. Due to production-induced decreasing reservoir pressures along with tight nature of the reservoir rock, development activities have focused more in placing wells towards the minimum horizontal stress direction and complete with multistage hydraulic fracturing aimed to improve lateral reservoir contact enabling higher production at sustained rates thereby increasing recovery with less number of wells to be drilled.

Horizontal wells drilled in the minimum horizontal stress direction are more challenging due to complex geological conditions and compressional in-situ stress conditions. The data shows that some wells were drilled without major difficulty while some other wells encountered more problems leading to stuck pipe events. A detailed study was conducted to identify the nature of these problems and ascertain major controlling factors for this variable drilling experience to make future operations safer and more efficient through recommendations based on a diagnostic analysis of the observed problems in existing wells.

Analysis of data suggests that excessive borehole breakouts and faster rate of penetration (ROP) are the key contributing factors to the observed drilling challenges. In addition, differential sticking has been found a potential risk across high porosity and/or depleted zones. As a result, optimum mud weight based on predrill geomechanics model was recommended to manage the hole stability. In addition, a safe limit of ROP as a function of hole azimuth was identified to manage efficient hole cleaning and avoid stuck pipe due to pack-off.  The recommendations made based on this analysis enabled successful drilling and timely completion of several horizontal wells across the field.

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With the implementation of new technology on deep-water units, there have been huge strides made towards a safer and more efficient way of drilling wells. However, with the introduction of these advancements different challenges also arise. In fact, as new technologies are introduced, the more important team skills, or Crew Resource Management becomes. With new technology, teams change and confidence in the new roles and responsibilities can suffer, which can effect communication and ultimately decision making. Piggy backing off the success of the aviation industry, at Maersk Training, we've found an efficient way to ensure that teams utilize each other to mitigate human error and more fluently adapt new technologies into their operations is by integrating three pieces of training. Specifically, we focus on merging high fidelity simulations with technical training and Crew Resource Management. This presentation highlights how to best train crews going into operations with MPD.

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Asphaltene deposition is a common cause of significant flow assurance problems in wellbores and production equipment as well as near-wellbore regions in oil reservoirs. Asphaltenes are surface-active and stabilize water-in-oil emulsions by flocculating and coagulating at interfaces. Commercial dispersants have been developed to mitigate asphaltene deposition, but their role on the interfacial properties of asphaltene films is unclear. My lab has generated micromodels, which are combined with high-speed imaging to visualize asphaltene deposition and transport in microfluidic devices with permeability contrasts, fractures, and multiple phases. The surfaces can be altered to mimic the chemistry and wettability found in reservoir systems. I will discuss how these devices can be utilized to advance our understanding of oil flow processes and design better strategies to address flow assurance problems.

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This webinar will focus on improving the reliability and performance of subsea insulation & downhole tools with Proxima® thermoset resins.  Aegion Corporation will discuss how Proxima filled its unmet needs for high performance thermal insulation for miles and miles of flowline. 

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

About Materia

Materia® provides Proxima® advanced polymer materials for the oil and gas industry. Applications include subsea insulation, buoyancy, and downhole tools.

The oil industry is in a period of transition during which companies, professionals, governments and academic institutions await what may be a new cycle in oil prices or a complete change in market dynamics due to policies, technologies and CO2 emission regulations.  More often, innovation has come up as a common topic either because of process optimization, cost reduction or for disruptive solutions. The industry’s production flow segments (upstream, midstream and downstream) projects and product lifecycles have specific technology demands, which compared to other markets, are conservative and slow to innovate and where success is measured in the number of patents.

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The oil and gas industry uses static and dynamic reservoir models to assess volumetrics and to help evaluate development options via production forecasts.   The models are routinely generated using sophisticated software.  Elegant geological models are generated without a full understanding the limitations imposed by the data or the underlying stochastic algorithms.  Key issues facing reservoir modelers that have been evaluated include use of reasonable semivariogram model parameters (a measure of heterogeneity), model grid size, and model complexity.  However, reservoir forecasts tend to be optimistic – a statement not provable with data in the public domain.  Yet, conversations at technical meetings, the lack of industry publications highlighting actual forecast accuracy, the development of more detailed reservoir models (presumably to yield better forecasts), all suggest that the industry could improve its reservoir performance forecast accuracy.  For example, dynamic models that use larger grid cells yield optimistic forecasts for some recovery processes as compared to forecasts obtained from models built with smaller grid sizes.  Also, the use of stochastic earth models and well placement optimization workflows will likely yield optimistic forecasts.  Overall, the impact of cell size, model parameters, inadequate use of analog data, and poorly constrained well location optimization may increase forecast optimism by 5-10 recovery factor units or more. Knowing what workflow aspects may contribute to forecast optimism should enable the industry to generate more reliable forecasts and make better use of capital.

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We are presenting a probabilistic approach to proactively adjust drilling parameters (which can be uncertain) in real time to help ensure the actual drilling path overlaps the target drilling path to the maximum extent. The planned well path is initially adjusted by a probabilistic formation earth model (EM) for maximum production potential and then adjusted to help improve well path smoothness to allow the passage of tubular strings. Drilling parameters and EM data can be updated in real time during drilling operations. Statistical methods are applied to quantify the uncertainty of the predicted actual well path.

Considering the uncertainty associated with petrophysical properties and drilling parameters, the overlap probability between actual and target well paths was computed and used as feedback for drilling parameter adjustments. This closed-loop feedback process enables proactive control of the actual well path. The drilling path simulation indicates that the trajectory resulting from using this new control method provides better reservoir access and maintains dogleg severity (DLS) at an acceptable level.

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