As the oil and gas industry is facing tumultuous challenges, adoption of cutting-edge digital technologies has been accelerated to deliver safer, more efficient operations with less impact on the environment. While advanced AI and other digital technologies have been rapidly evolving in many fields in the industry, the HSE sector is playing catch-up. With the increasing complexity of risks and safety management processes, the effective application of data-driven technologies has become significantly harder, particularly for international organizations with varying levels of digital readiness across diverse global operations. Leaders are more cautious to implement solutions that are not fit-for purpose, due to concerns over inconsistencies in rolling out the program across international markets and the impact this may have on ongoing operations. This paper describes how the effective application of Artificial intelligence (AI) and Machine Learning (ML) technologies have been used to engineer a solution that fully digitizes and automates the end-to-end offshore behavior-based safety program across a global offshore fleet; optimizing a critical safety process used by many leading oil & gas organization to drive positive workplace safety culture. The complex safety program has been transformed into clear, efficient and automated workflow, with real-time analytics and live transparent dashboards which detail critical safety indicators in real time, aiding decision-making and improving operational performance. The novel behavior-based safety digital solution, referred to as 3C observation tool within Noble drilling, has been built to be fully aligned with the organization's safety management system requirements and procedures, using modern and agile tools and applications for fully scalability and easy deployment. It has been critical in sharpening the offshore safety observation program across global operations, resulting in a boost of the workforce engagement by 30%, and subsequently increasing safety awareness skill set attainment; improving overall offshore safety culture, all while reducing operating costs by up to 70% and cutting carbon footprint through the elimination of 15,000 manhours and half a million paper cards each year, when compared to previously used methods and workflows

Working towards a net zero future requires change and adaptation from us all. Innovation offers many potential solutions on how to successfully make that change within the oil and gas industry. Consequently, maximising the value that technological innovation presents is vital for delivering a sustainable net zero. Yet, the oil and gas industry has developed a reputation for being conservative and reluctant to adopt new technology, with companies sometimes referred to as "fast followers". In recent years, the industry has begun to change with an incremental increase in innovation activities. Despite these efforts, and a need to accelerate innovation, there appears to be a resistance to adopt new technology. Evidence from O&G industry bodies indicate that psychological factors play a key role in technology adoption; not surprisingly, as workers, managers, investors, and regulators can all have a powerful influence on an organisation's receptivity to new technology. Recent research has provided insight into the psychological factors that influence technology uptake decisions in the oil and gas industry. Through a series of studies, the psychological technology adoption framework (P-TAF) was developed which outlined the 15 key psychological factors that influence technology adoption decisions. These are organised into 6 categories: personality, attitudes, motivations, cognitive factors, social factors, and organisational level factors. The work emphasised the influence that overarching organisational culture can have on how people respond to and introduce technology within their company. Whilst technology readiness levels are commonly applied to start-ups and their innovations, less is known about the readiness culture which facilitates innovation uptake. To bridge this gap, a preliminary measure of organisational innovation adoption culture was developed as based upon the previous psychological research, empirical innovation measures, and organisational culture models. This was piloted as an online survey with 36 people working in the technology space in O&G in June 2020. These results were used to later refine the culture measure to develop a 33-item scale consisting of eight categories. This new measure was deployed as part of an industry benchmarking study of innovation adoption culture within O&G consisting of 82 managers from 12 companies and in December 2020. Participating organisations were given the opportunity to receive a snapshot of their technology adoption culture. An overview of the measure and a summary of survey results will be given during the presentation as well as recommendations on how to support an innovation adoption culture. A considerable volume of new technology needs to be developed and adopted to be able to reach net zero and understanding the psychological and cultural barriers is imperative to delivering that.

Well clean-up is one of the most complex operations performed at the wellsite today. During clean-up, a well is flowing for the first time after initial completion or workover operations through temporary surface facilities to either conduct a welltest or to simply condition the well before connecting it to production facilities. Currently, there are no practical recommendations available that would summarize clean-up experiences and guide operating companies through the process of efficiently planning well clean-up operations. Conventional well clean-up operations are inherently challenging owing to the requirements for accurate data measurements, safe handling and disposal of produced fluids (hydrocarbons, completion brine, water, and solids). Experience has shown that it is nearly impossible to perform well clean up within pre-defined constraints and target criteria without an appropriate design, equipment selection and operations planning to account for the specificities of each situation. Steady-state flow simulators have been the standard tool to model pressure and temperature changes along the wellbore and through temporary production system during well clean-up process. Those assume either final stabilized conditions or a limited number of intermediate ones and formed the basis for equipment selection. But this approach has critical limitations in modelling flowing well behavior and fast-changing flowing conditions, and therefore in assessing operational flow assurance risks and the dynamic capability of the surface plant to handle produced fluids. The paper describes in detail today's challenges during well clean-up operations that combine the need for operational safety, minimal environmental footprint and flow assurance considerations that have to be balanced with costs and production performance optimization. The paper provides practical recommendations and presents multiple case studies highlighting the results and lessons learned from applying a novel, unique workflow based on the application of a transient-multiphase flow simulator. Combined with modern well-testing equipment such as modern test separators, remotely actuated adjustable chokes or environmentally friendly fluid disposal techniques, such advanced design allows performing clean-up operations efficiently while remaining within time, rates, pressure or emissions limits.

Objectives/Scope Mature fields operations, which are almost 70% of today's production have a high water cut content. For each barrel of oil produced there can be 3 or more barrels of water. This means that operational conditions are challenging and might not be ideal for the facilities. In crude oil tanks, one of the most crucial operation parameter is the settling time of oil and water. Especially with heavy oils and high water cut, the operational conditions can be challenging with the existence of emulsion/rag layers in the tank. Most common level detection instrumentation struggle with detecting the proper interface levels leading to faulty control that can cause costly remediation and loss of revenue. This paper presents a novel solution by applying electric tomography pipe and probe sensors. Methods, Procedures, Process In electrical tomography, multiple electrodes are attached on the surface of the sensor and excitations are applied to some electrodes and responses are measured from other electrodes. Assuming a fast separation in the following crude oil tank, the operator expects the flow being stratified already in incoming trunk line. In real life this is not often the case: The incoming flow is turbulent meaning that there is no clear water/oil interface. To overcome this a pipe sensor is needed to monitor the flow regime and hence there is a possibility to control the a) chemical feed and b) flow speed to get the flow stratified. As soon as the flow is stratified in a trunk line it will be guided to a crude oil settling tank for an additional separation. In this tank there is a possibility to apply a probe sensor to monitor reliably the emulsion layer between water and oil. This allows settling time, process parameters and chemicals to be optimized to get a clear separation and hence improving the oil and water quality for a further processing. Results, Observations, Conclusions Results from pipe sensor operation in crude oil pipelines will be shared. The results will show an accurate water cut profile across the pipe cross section even under stratified flow conditions. Additionally, probe sensor results in a crude oil tank operation will be shared and hence confirming the reliability and robustness of the probe sensor operation in tanks. One of the key features of the pipe and probe sensors is the full functionality even under severe contamination with deposits on the sensor surfaces. The operational principle of this method will be shared and verified by experimental results. Novel/Additive Information The sensor technology for the tank inspection and piping uses novel electrical tomography with compact electronic and fast-acting computation with high resolution. This type of technology for settling tank application is new.

Robotics is often referred to as an enabler towards safer and more cost-effective operations within the energy industry. However, for robots to achieve their full potential they too require their own enablers. This paper intends to present a collection of lessons learnt in robotic development to date that has resulted in the necessity to develop an encompassing digital architecture. This architecture, with an artificial intelligence component, has been designed to optimise both robot and digital twin capability, where the complete system is always working with the latest information available that reflects the ground truth. The architecture concept will be explained including why integration and standardisation are of paramount importance. The paper will also demonstrate how business value can be generated before fully explainable, transparent, verifiable autonomy is available for robotics to be deployed on future unmanned platforms that have been designed to optimise robot operation opposed to existing human engineered environments. All these topics are important within their own right, but all are also years away from adoption on an industrial scale. To ensure continued engagement until such a point is reached, the industry needs to focus more on the quick wins and immediate value that can be gained from robotics. The impact of COVID-19 has shown how quickly and effectively the energy industry has been able to transition to remote working. This represents a massive opportunity to prove what robotics can deliver via remote operation, minimizing vendors where possible. The concept of "full" autonomy will be discussed with respect to value generation and barrier to entry or adoption within the energy industry. The paper will address all practical considerations that have required attention to date plus explain the next programme of work in the evolving RAS (robotics and autonomous systems) digital architecture whilst ensuring complete integration with the data pipelines that have already been built.

The paper describes a project known internally as "InWell" to address multiple requirements in Repsol Drilling & Completions. InWell is defined by a new Operating Model comprising Governance, People, Process, Functions and Technology. This paper addresses changes to the Technology element - often referred to as "Digitalization". The paper includes a discussion about the business transformation strategy and case studies for addressing three of 18 functionalities identified in the first round of development. The InWell development strategy followed four steps; identification of performance issues, envisioning of a future operating model, identification of functionalities required/supporting this operating model and matching to digital solutions. Our case studies focus on three functionalities provided by three separate companies, Unification of Planning and Compliance, Real Time Data aggregation and Key Performance Indicators. Each functionality was addressed with an existing commercial application customized to meet specific requirements. A corporate web-based Well Construction Process (WCP) was initially piloted and then extended to include all well projects. The WCP identifies the key Tasks that must be completed per project, and these are all tracked. Data from this application is used by a third-party Business Analytics application via an API. Real time data from many sites and a wide range of sources was aggregated and standardized, Quality Controlled and stored within a private secure cloud. The data collation service is an essential building block for current third-party applications such as the operating centre and is a prerequisite for the goal of increased automation. A suite of Operator specific Key Performance Indicators (KPIs) and data analytics services were developed for drilling and completions. Homogenized KPIs for all business units provide data for objective performance management and apples-to-apples comparison. Results are presented via custom dashboards, reports, and integrations with third party applications to meet a wide range of requirements. During a four-month Pilot Phase the InWell Project delivered € 2.5 million in tangible savings through improvements in operational performance. In the first 12 months € 16 million in savings were attributed to InWell. By 2022 forecast savings are expected to exceed € 60 million ( Figures 1 & 2 ). The value of Intangible benefits is thought to exceed these objective savings. Figure 1 The Business Case for InWell – Actual & Projected Savings and Costs. Figure 2 InWell Services addressing Value Levers and quantified potential impact. A multi-sourced digital strategy can produce quick gains, is easily adapted, and provides high value at low risk. The full benefit of digital transformation can only be realised when supported by an effective business operating model. The paper describes a project known internally as "InWell" to address multiple requirements in Repsol Drilling & Completions. InWell is defined by a new Operating Model comprising Governance, People, Process, Functions and Technology. This paper addresses changes to the Technology element - often referred to as "Digitalization". The paper includes a discussion about the business transformation strategy and case studies for addressing three of 18 functionalities identified in the first round of development. The InWell development strategy followed four steps; identification of performance issues, envisioning of a future operating model, identification of functionalities required/supporting this operating model and matching to digital solutions. Our case studies focus on three functionalities provided by three separate companies, Unification of Planning and Compliance, Real Time Data aggregation and Key Performance Indicators. Each functionality was addressed with an existing commercial application customized to meet specific requirements. A corporate web-based Well Construction Process (WCP) was initially piloted and then extended to include all well projects. The WCP identifies the key Tasks that must be completed per project, and these are all tracked. Data from this application is used by a third-party Business Analytics application via an API. Real time data from many sites and a wide range of sources was aggregated and standardized, Quality Controlled and stored within a private secure cloud. The data collation service is an essential building block for current third-party applications such as the operating centre and is a prerequisite for the goal of increased automation. A suite of Operator specific Key Performance Indicators (KPIs) and data analytics services were developed for drilling and completions. Homogenized KPIs for all business units provide data for objective performance management and apples-to-apples comparison. Results are presented via custom dashboards, reports, and integrations with third party applications to meet a wide range of requirements. During a four-month Pilot Phase the InWell Project delivered € 2.5 million in tangible savings through improvements in operational performance. In the first 12 months € 16 million in savings were attributed to InWell. By 2022 forecast savings are expected to exceed € 60 million ( Figures 1 & 2 ). The value of Intangible benefits is thought to exceed these objective savings. Figure 1 The Business Case for InWell – Actual & Projected Savings and Costs. Figure 2 InWell Services addressing Value Levers and quantified potential impact. A multi-sourced digital strategy can produce quick gains, is easily adapted, and provides high value at low risk. The full benefit of digital transformation can only be realised when supported by an effective business operating model.

Oil & gas (O&G) operators are increasingly focused on decarbonization and reaching net-zero carbon emissions. The O&G industry seeks to minimise methane emissions. Verification of estimated emissions using top down measurement methods represents a critical component of this effort. A novel approach to operationalizing top-down emissions surveys was developed and demonstrated, leveraging expertise in unmanned vehicle application, innovative methane emissions measurement technology, and an O&G industry collaborator. The inspection technique utilizes a fixed-wing unmanned aircraft to perform a remote offshore asset inspection while safely launching and recovering onshore. This method enables the collection of many tens of thousands individual point methane concentration measurements and affords the ability to resolve facility-level methane emissions and in conjunction with appropriate environmental conditions information, derive an accurate emission rate for an individual asset, while accounting for background fluctuation and potential upwind sources.The unmanned aircraft does not require any crew or equipment to be taken offshore or make modifications to the asset, thus allowing inspections to be performed with minimum impact to facility operations. This work overcame significant regulatory hurdles to fly long distance unmanned aircraft in congested airspace, developed detailed operational procedures and demonstrated the safety of the technique to both the O&G and aviation community, and the effectiveness of the measurement technology. The work demonstrated the suitability of the technique for operationalisation for routine measurement programmes.

Optimising the Rate of Penetration (ROP) on Development wells contributes heavily to delivery of projects ahead of schedule and has long been a goal for drilling engineers. Selecting the best parameters to achieve this has often proved difficult due to the extensive quantities of data concerning formation types, bottom-hole assembly (BHA) design and bit specifications. Legacy drilling data can also be vast and not well characterised, making it very difficult to robustly analyse manually. Additionally, multiple stakeholders can each have their own hypotheses on how to improve drilling performance, including bit vendors, directional drilling companies, drilling engineers and offshore supervisors, creating further confusion in this field. Together with its team of data scientists, TotalEnergies E&P UK (TEPUK) has utilised machine learning to analyse field and equipment data and produce guidelines for optimised drilling rate. The machine learning algorithm identifies parameters which have a statistical likelihood of improving ROP performance whilst drilling. The model was developed using offset well data from TotalEnergies' Realtime Support Centre (RTSC) and bit design information. This represented the first use of Machine Learning in the 20+ years of drilling on Elgin Franklin. Adapting to this new data-based method forms part of a wider digital revolution within TEPUK and the Offshore Drilling Industry. In this case, an integrated approach from the data scientists, drilling engineers and supervisors was required to transition to a new way of working. The first trial of using optimised parameters was on a recent Franklin well (F13) in the Cretaceous Chalk formations. The model generated statistically optimised parameter sheets which were strictly executed on site. Within the guideline sheets were suggested ranges of Revolutions per Minute (RPM), Flowrate, Weight on Bit (WOB) and Torque, as well as recommendations for bit blades and cutters. Heatmaps were generated to show what combination of WOB and RPM would likely achieve best ROP in each sub formation. The parameter range defined was specifically narrow to reduce any time spent varying parameters. In practice the new digital approach was successfully adopted offshore and contributed to the delivery of the 12 ½" and 8 ½" sections in record time for the field, resulting in significant savings versus AFE. Following the success of the guideline implementation, steps have been taken to integrate the machine learning model with live incoming data on TotalEnergies' digital drilling online platform. Since the initial trial on Franklin, online ROP optimisation features have been deployed on the Elgin field and currently provide live parameter guidance, a forecast to section TD and data driven bit change scenario analyses whist drilling.

Maximizing operational efficiency is a critical challenge in oil and gas production, particularly important for mature assets in the North Sea. The causes of production shortfalls are numerous, distributed across a wide range of disciplines, technical and non-technical causes. The primary reason to apply Natural Language Processing (NLP) and text mining on several years of shortfall history was the need to support efficiently the evaluation of digital transformation use-case screenings and value mapping exercises, through a proper mapping of the issues faced. Obviously, this mapping contributed as well to reflect on operational surveillance and maintenance strategies to reduce the production shortfalls. This paper presents a methodology where the historical records of descriptions, comments and results of investigation regarding production shortfalls are revisited, adding to existing shortfall classifications and statistics, in particular in two domains: richer first root-cause mapping, and a series of advanced visualizations and analytics. The methodology put in place uses natural-language pre-processing techniques, combined with keyword-based text-mining and classification techniques. The limitations associated to the size and quality of these language datasets will be described, and the results discussed, highlighting the value of reaching high level of data granularity while defeating the ‘more information, less attention’ bias. At the same time, visual designs are introduced to display efficiently the different dimensions of this data (impact, frequency evolution through time, location in term of field and affected systems, root causes and other cause-related categories). The ambition in the domain of visualization is to create User Experience-friendly shortfall analytics, that can be displayed in smart rooms and collaborative rooms, where display's efficiency is higher when user-interactions are kept minimal, number of charts is limited and multiple dimensions do not collide. The paper is based on several applications across the North Sea. This case study and the associated lessons learned regarding natural language processing and text mining applied to similar technical concise data are answering several frequently asked questions on the value of the textual data records gathered over years.

Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.

One emerging application in geothermal energy is that of closed-loop systems, where two laterals are intersected so that a working fluid can be pumped down one wellhead and up another. These solutions are attractive because they do not rely on the natural permeability of a formation or a reservoir of heated water already in place, they simply require a high enough downhole temperature. While a great deal of discussion exists on wellbore intersection, most applications are by their nature heavily constrained by tight geologic requirements (e.g. coal-bed methane) or have one wellbore trajectory rigidly defined (e.g. relief well drilling). These intersection operations require extensive use of specialized ranging technologies and control drilling at the intersection point which can be time-consuming. Closed-loop geothermal presents a unique opportunity, with relatively few constraints to satisfy (e.g. target depth, lateral length). This study uses this freedom in trajectory design and quantifies the extent that various wellbore positioning techniques can increase the probability of intersection while minimizing the need for ranging workflows. A baseline scenario is described, with wells originating from differing pad locations, drilling with standard practices and active magnetic ranging. Using Monte Carlo techniques, the probability of successful intercept is evaluated for alternate trajectory combinations and compared to the baseline. These include well pairs originating from the same pad and pairs from differing pad locations. Major factors contributing to relative survey errors are identified and the impact of uncertainty reducing techniques are explored for each trajectory type. Techniques include survey corrections, variation of the trajectory profiles, incidence angle at intersection, and the use of alternative solutions to control relative vertical uncertainty. For each scenario, the probability of intercept was evaluated for cases without using ranging tools and for both passive and active ranging technologies. A cost-benefit comparison is conducted, and an optimal combination of factors is identified. For the baseline scenario, low probabilities of collision imply that extensive use of ranging is required for a successful operation. Positional uncertainty reduction techniques and multiple target intervals can greatly increase the collision probability and reduce the need for ranging. Of importance to increasing the probability of successful interception are techniques that maximize the uncertainty reduction along a single axis (e.g. the vertical plane). This enables a "sweep" across the other plane to achieve intersection. Value provided by additional uncertainty reduction techniques depends on the assumed costs of drilling additional footage, performing ranging operations, and rig spread rate. The application of sophisticated wellbore positioning techniques at scale to the closed-loop geothermal problem has not been previously explored. The relatively low number of constraints compared to traditional wellbore intersections enables strategies not otherwise available for successful project construction.

Objectives / Scope Re-wheeling compressors to match late-life field conditions gives significant benefits in operational efficiency and carbon reduction. But changing the compressor wheels and increasing shaft speeds also introduces a risk in terms of the rotor-dynamic stability of the system. API assessments use deterministic methods to assess the design change, but give less information in terms of the key risks and how to control them. This paper outlines new methods for assessing rotor dynamic risks to compressors during re-wheeling and their value over traditional methods. Methods New methods were developed to extend beyond the API requirements in order to assess and manage the rotor-dynamic risk as part of a peer review process of re-wheeling a compressor train. A combination of sensitivity studies on key parameters and Self Organizing Maps (SOMs - a machine learning technique) was used to identify the factors which present the greatest risk to the re-wheeling, and a Monte Carlo analysis was used to identify the change in risk of rotor-dynamic problems when compared with the existing machine. Results The Monte Carlo analysis used random distributions of factors on key input parameters, and the same factors were applied to the existing and re-wheeled designs. It identified that although the re-wheeled design was nominally more stable than the existing design according to the API analysis, it actually presented a greater risk of instability. This is because the distribution of resulting stability values had a higher mean but a greater spread than the existing machine when subject to uncertainty in input parameters. Since the existing machine is free from dynamics problems, the parameter combinations which resulted in an unstable existing machine could be discounted, but the resulting subset of factors when applied to the re-wheeled design still gave some unstable cases. Therefore, the fact that the existing machine is free from dynamics problems does not in itself discount the possibility of problems following the re-wheel. SOMs were used to identify the components which posed the greatest risk to the re-wheeled design. This highlighted that low stiffness in two particular bearings along the high speed shaft would pose the greatest risk to shaft stability, meaning that close attention can be paid by the operators and OEMs to this to manage the risks as the re-wheel progresses. Novel Information This work shows that probabilistic and machine learning techniques have significant value in managing risks during compressor re-wheeling, highlighting risks which would not be identified using standard deterministic methods and focusing attention on the aspects which are most important to manage them.

Nearly all oil and gas operators and engineering companies in the offshore sector today are engaged in programs to advance concepts for low-manned and/or normally unattended production installations (NUIs). When it comes to the design of these facilities, topsides rotating equipment and electrical, instrumentation, control, and telecommunications (EICT) packages represent key areas of interest for decision-makers, owing to the significant impact they can have on required manning levels. Over the past decade, the author's company has worked closely with major Operators in the U.S. and the North Sea to look at how existing technologies can be applied in these areas to safely facilitate de-manning of both brownfields and greenfields. This paper provides insight into these efforts. It also presents projected manpower and cost savings from de-manning, using data derived from both studies and real-world projects.

Fiber Optic (FO) sensing capabilities for downhole monitoring include, among other techniques, Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS). The appeal of DTS and DAS data is based on its high temporal and spatial sampling, allowing for very fine localization of processes in a wellbore. Furthermore, the broad frequency spectrum that especially DAS data is acquired with, enables observations, ranging from more continuous effects like oil flow, to more distinct effects like opening and closing of valves. Due to the high data volume of hundreds of Gb per well per hour, DAS data has traditionally been acquired acquisition-based, where data is recorded for a limited amount of time and processed at a later point in time. This limits the decision-making capability based on this data as reacting to events is only possible long after the event occurred. Equinor has addressed these decision-making shortcomings by building a real-time streaming solution for transferring, processing, and interpretation of its FO data at the Johan Sverdrup field in the North Sea. The streaming solution for FO data consists of offshore interrogators streaming raw DAS and DTS data via a dedicated bandwidth to an onshore processing cluster. There, DAS data is transformed into FO feature data, e.g., Frequency Band Energies, which are heavily decimated versions of the raw data; allowing insight extraction, while significantly reducing data volumes. DTS and DAS FO feature data are then streamed to a custom-made, cloud-based visualization and integration platform. This cloud-based platform allows efficient inspection of large data sets, control and evaluation of applications based on these data, and sharing of FO data within the Johan Sverdrup asset. During the last year, this FO data streaming pipeline has processed several tens of PB of FO data, monitoring a range of well operations and processes. Qualitatively, the benefits and potential of the real-time data acquisitions have been illustrated by providing a greater understanding of current well conditions and processes. Alongside the FO data pipeline, multiple prototype applications have been developed for automated monitoring of Gas Lift Valves, Safety Valve operations, Gas Lift rate estimation, and monitoring production start-up, all providing insights in real-time. For certain use cases, such as monitoring production start-up, the FO data provides a previously non-existent monitoring solution. In this paper, we will discuss in detail the FO data pipeline architecture from-platform-to-cloud, illustrate several data examples, and discuss the way-forward for "real-time" FO data analytics.

When an oilfield is exploited by simply producing oil and gas from a number of wells, the reservoir pressure in many circumstances drops quicker than normal impacting the production rates ( Koning, 1988 ) and well performance. To maintain the pressures in the oil producing formations, waterflooding enhancement method is implemented by the Operators. This is achieved by drilling injection wells or converting the oil producing wells into injectors. The injection wells are located at carefully selected points in the oilfield so that the water displaces as much oil as possible to the production wells before the water starts to break through. A significant saving in an oilfield development can be obtained by reducing the actual number of injecting wells and increasing each of the injector wells' capacity for injection. Balancing the injection and produced volumes often involves injecting at high pressures leading to the fracture of the reservoir rocks along a plane intersecting the wellbore. This happens when injection pressure overcomes the rock stress and its tensile strength, thereby creating an induced fracture network. With continuous injection, these fractures start propagating into the reservoir and may reach the reservoir caprock. Continuing to inject further in such a fracture system may breach the top seal integrity of the caprock leading to uncontrolled out of zone injection. The study of evaluation of downhole fracture monitoring is divided into two parts. In this paper a downhole verification approach to identify the fracture initiation point(s) is the focus. It describes the planning, execution and interpretation of the downhole data. This includes spectral acoustic monitoring and modelling of the temperature responses to quantify the injectivity profile. In paper ( Kohli, Kelder, Volkov, Castelijns, & van Eijs, 2021 ), the direct business impact and regulatory requirements are discussed by further integration of acoustic monitoring and temperature modeling data with detailed results from downhole measurements of fracture dimensions by means of pressure fall off tests. Combined, both studies form the integrated approach that the Operator took to meet the regulatory requirements proving that the fracture network propagation remains within the reservoir and that the top seal integrity is maintained.

We introduce a novel Machine Learning (ML) approach for processing distributed fiber-optic sensing (DFOS) data that enables dynamic flow profile monitoring using a fiber-optic e-line cable deployed in a gas condensate well and compare it to a conventional approach. DFOS technology has the potential to provide more efficient and dynamic flow profiles compared to traditional methods, particularly in high rate gas wells where production logs (PL) are recorded at reduced rates to avoid tool lifting. Distributed acoustic and temperature sensing (DAS & DTS) data were acquired simultaneously while the well was producing ~70 MMSCF/D gas. Conventional PL data was also acquired under the same condition to validate the flow profiling results obtained from DFOS measurements. This paper describes a novel data processing approach where ML based models for pattern recognition were applied to obtain the signatures of different fluid types. Flow profiling is achieved by applying multiple data models to address three key questions for inflow profiling: (1) which zones are producing? (2) what is the phase? and (3) what is the flow rate? A blind test was set up to avoid results contamination. The processing and interpretation of DFOS data and PL data were carried out independently and results were compared only when the work on both datasets was completed. The comparison demonstrates a good match between two measurements for gas inflow profile with an average error of about 1% in relative gas rate allocation along the four producing perforated intervals. Flow profile in a single-phase gas producing well was accurately determined by DFOS data analysis and the liquid production rate was then re-calculated using condensate-gas ratio (CGR) to obtain liquid and gas production rates at standard surface condition. The well was connected to a test separator during the entire acquisition period, and accurate gas, condensate and water production rates were obtained in real-time at surface condition. The hybrid processing technique was applied for the first time among our well stock and resulted in accurate gas inflow profiling. To further validate the performance of the presented approach, the authors intend to repeat the test in other high rate gas producing wells, including wells with permanently installed fiber. Multi-disciplinary teamwork involved collaboration between operator and vendor and allowed for efficient operational execution. The result of the risk assessment ensured the selection of the best candidate well ensuring minimum sand production at the optimum production rate, optimization of stationary time for DFOS data acquisition and cable armor erosion model.

The O&G industry has been producing hydrocarbons from subsea reservoirs for several decades. However, there is a technological gap in the ability to reliably detect and quantify dissolved gases within the water column. This technological gap has in turn led to a scientific gap in our ability to determine the subsurface origin of subsea fluid emissions. Gas releases are commonly found in the marine environment primarily because of naturally occurring seeps and occasionally due to Oil and Gas production activities. There is a need to be able to identify the gas composition and accurately characterize its source (i.e., ongoing microbial activity or thermogenic derived hydrocarbons). However, building a reliable solution which allows this differentiation between thermal and microbial sources in the underwater environment as well as the inference of their subsurface origin requires a multi-disciplinary subsurface workflow coupled comprehensive high-fidelity measurements at the seabed. As one of the front-end building blocks of any robust multi-disciplinary workflow, there is a need for development of an in-situ sensing and sampling capability which allows real-time assessment and geological characterization of the underwater emissions across the upstream industry, from exploration to abandonment. Such a capability would also be complementary to the geohazard and subsurface assessment practices e.g., by reducing lost rig time during interventions by allowing quick characterization of emissions that arise from natural seeps or LOPC (Loss of Primary Containment) events. This paper describes the maturation of a compact underwater in-situ sensing technology deployed from autonomous or tethered underwater vehicles and which enables measurements of gas constituents and their respective isotopes at the seabed.

There is a growing interest in electrification of offshore production assets using renewable energy devices. Typical configurations have arrays of power generating devices, located a short distance away from the platform and linked by subsea cables. To date, floating offshore wind turbines and wave energy converters have been trialed as sources of electrical power. With hulls, moorings and cables set out in close proximity, there is a unique station keeping risk profile that needs to be managed, to ensure fitness for service through the life of the field. When developing electrification schemes, there is a body of relevant research that can be accessed to help identify and control mooring risks. In the early days of North Sea floating production, the industry encountered multiple instances of accelerated mooring line degradation. The causes and mitigations were researched through joint industry projects, leading to the publication of operating guidance through OGUK, the HSE, ISO and others. This guidance, along with a series of milestone papers, offers a valuable body of learning that is available to electrification projects. In this paper, the authors survey the integrity threats and challenges affecting moorings in electrification schemes that are based around small arrays of wind turbines or wave energy converters. Through qualitative risk assessment, comparison is made to mooring integrity issues in the floating production sector. The relevance of guidance from oil and gas is reviewed. Consideration is given to the consequences of a line failure, and how in some cases these may represent intolerable risks. It is argued that a through-life integrity risk assessment is essential for successful deployments; also, that existing OGUK guidance on mooring integrity be extended, to reflect the specifics of electrification projects.

In the wake of today's industrial revolution, many advanced technologies and techniques have been developed to address the complex challenges in well integrity evaluation. One of the most prominent innovations is the integration of physics-based data science for robust downhole measurements. This paper introduces a promising breakthrough in electromagnetism-based corrosion imaging using physics informed machine learning (PIML), tested and validated on the cross-sections of real metal casings/tubing with defects of various sizes, locations, and spacing. Unlike existing electromagnetism-based inspection tools, where only circumferential average metal thickness is measured, this research investigates the artificial intelligence (AI)-assisted interpretation of a unique arrangement of electromagnetic (EM) sensors. This facilitates the development of a novel solution for through-tubing corrosion imaging that enhances defect detection with pixel-level accuracy. The developed framework incorporates a finite-difference time-domain (FDTD)-based EM forward solver and an artificial neural network (ANN), namely the long short-term memory recurrent neural network (LSTM-RNN). The ANN is trained using the results generated from the FDTD solver, which simulates sensor readings for different scenarios of defects. The integration of the array EM-sensor responses and an ANN enabled generalizable and accurate measurements of metal loss percentage across various experimental defects. It also enabled the precise predictions of the defects’ aperture sizes, numbers, and locations in 360-degree coverage. Results were plotted in customized 2D heat-maps for any desired cross-section of the test casings. Further analysis of different techniques demonstrated that the LSTM-RNN could show higher precision and robustness compared to regular dense NNs, especially in the case of multiple defects. The LSTM-RNN is validated using additional data from simulated and experimental data. The results show reliable predictions even with limited training data. The model accurately predicted defects of larger and smaller sizes that were intentionally excluded from the training data to demonstrate generalizability. This highlights a major advance toward corrosion imaging behind tubing. This novel technique paves the way for the use of similar concepts on other sensors in multiple barriers imaging. Further work includes improvement to the sensor package and ANNs by adding a third dimension to the imaging capabilities to produce 3D images of defects on casings.

Drilling microchips are millimeter-size sensing devices, capable of measuring in-situ downhole temperature, and at the same time, withstanding harsh downhole conditions. In this work, 140 microchips were dropped from the drill pipe during the connections. The devices travel through the bottomhole assembly (BHA), drill bit, annulus, and eventually get recovered at the shale shaker. A total of 80 microchips were recovered at the shaker, which resulted in a physical recovery rate of 57%. The microchip recorded the dynamic temperature profile of the entire wellbore including a long openhole section only a few hours before the well turned into total loss. The data downloaded from the microchip shows an excellent consistency throughout the three tests. The measured dynamic bottomhole temperature provides a correction of 10 deg F to the best practice of the industry in terms of downhole thermal simulation, offering valuable measured input for the optimization of thermal activated LCMs or cementing job. To our best knowledge, it is the industry's first successful attempt in logging an openhole section in a highly loss zone. The microchip recorded the dynamic temperature profile of a long open hole only a few hours before the well turned into a total loss. Due to the lack of industrial solutions for downhole temperature measurement under such conditions, the microchip technology showed unique advantage for critical applications, especially in operations with highly valued assets.