In the recurring need to optimize drilling operations and reduce costs, a full RTOC (Real Time Operations Center) solution was deployed as part of the organization structure. To bring accurate, automatic and relatable data capture from surface sensors, the RTOC introduced a digital twin approach to improve field to town collaboration. The paper will demonstrate the benefits brought to operations by the solution in terms of risk identification and lessons learnt. RTOC digital twin solution integrates standard physical models’ workflows for hydraulics, torque and drag with advanced solutions using machine-learning algorithms. Capitalizing on operations recognition algorithm, the solution identifies thresholds and calibrates parameters to automatically classify operations into "Rig States" and "Drill States". The algorithm is trained to identify operational sequences and can derive complex measurements like downhole weight-on-bit and torque that are in turn fed into different workflows. This holistic event-based torque and drag baseline determination is used to define hole cleaning roadmap with minimum manual inputs. RTOC receives, processes and publishes the real time data on through its platform for all drilling and completion operations. This continuous process has enabled drilling operations team to assess and intervene on a need basis thanks to the clear event identification it offers. Amongst the digital workflows, the hole cleaning roadmap, combines modelled and automatically identified torque and drag data points rendered and shared with the stakeholders to ensure the capture of deviations and framing of potential risks to acceptable levels through a common decision platform. The clear output of single identifiable drilling event (such as pick up, slack off and free rotating weight) provides constant fact-based data for an adequate protocol to run casings and liners and refine engineering designs. In turn it has enabled to break casing and liner run records in their different operating fields. The drilling efficiency roadmap rely on quantitative algorithm and reliable output of downhole weight-on-bit, downhole torque and mechanical specific energy with automatic calibration, without user intervention nor bottom-hole-assembly modelling, allowing to substitute actual downhole measurements. This has been a performance enhancer in the improvement of rate of penetration regardless of the availability of downhole sensors. This new approach based on modern data science and digital twin based on a robust method, provides with a consistent and clear outcome regardless of service providers involved in the direct operations. It was trained, tested and validated prior to deployment, on more than 80 wells. This has also made possible the introduction of other algorithmic developments for Realtime dynamic modelling.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe poor productivity and abnormal productivity decline in some SINCOR horizontal wells in the Faja field could be traced back to drilling and well completion practices.The wells were drilled with solid-free XCD mud and completed with slotted liners. Drilling operation is characterized by severe mud losses. The wells were brought into production without wellbore cleanout to remove the drilling mud induced damage. The wells produce Extra Heavy Oil (EHO). The observed trends since the start-up of production was indicative of the skin damage magnitude. An in-house review in 2005 adopted an approach that included acquiring and extending information derived from cores and reservoir fluids, laboratory analysis data, drilling, well completion/well test results, and production data. X-ray diffraction studies show that the formation contains about 8% of clay minerals which comprise mostly kaolinite (~85%) with traces of illite and smectite. The damage mechanisms identified included fines migration, the swelling of reactive clay minerals, physical blockage by unremoved filter-cake masses, water-block resulting from massive leakoff of aqueous mud filtrate, the wax crystallization and asphaltene precipitation blockage. Engineering work and technology-based solution which encompass diagnosis of damage mechanism, laboratory testing, formulation and optimization of treatment fluids, job design and field execution procedure were carried out to mitigate the formation damage. The surfactant-diesel based (SDB) treatment design and application has yielded great successes in removal of formation damage, resulting in a high incremental oil production with job payback time of 3 -24 days. Evaluation of the two SDB treatment campaigns conducted in 2005/2006 revealed that water-block with its associated high Interfacial Tension against the EHO constitutes a serious impediment to oil productivity.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe poor productivity and abnormal productivity decline in some SINCOR horizontal wells in the Faja field could be traced back to drilling and well completion practices.The wells were drilled with solid-free XCD mud and completed with slotted liners. Drilling operation is characterized by severe mud losses. The wells were brought into production without wellbore cleanout to remove the drilling mud induced damage. The wells produce Extra Heavy Oil (EHO). The observed trends since the start-up of production was indicative of the skin damage magnitude. An in-house review in 2005 adopted an approach that included acquiring and extending information derived from cores and reservoir fluids, laboratory analysis data, drilling, well completion/well test results, and production data. X-ray diffraction studies show that the formation contains about 8% of clay minerals which comprise mostly kaolinite (~85%) with traces of illite and smectite. The damage mechanisms identified included fines migration, the swelling of reactive clay minerals, physical blockage by unremoved filter-cake masses, water-block resulting from massive leakoff of aqueous mud filtrate, the wax crystallization and asphaltene precipitation blockage. Engineering work and technology-based solution which encompass diagnosis of damage mechanism, laboratory testing, formulation and optimization of treatment fluids, job design and field execution procedure were carried out to mitigate the formation damage. The surfactant-diesel based (SDB) treatment design and application has yielded great successes in removal of formation damage, resulting in a high incremental oil production with job payback time of 3 -24 days. Evaluation of the two SDB treatment campaigns conducted in 2005/2006 revealed that water-block with its associated high Interfacial Tension against the EHO constitutes a serious impediment to oil productivity.
Majority of organizations endeavor to reduce operating costs and improve operational efficiencies. The concept of Mechanical Specific Energy (MSE) has long been implemented in the industry to improve drilling performance. The Drilling Real time Operations Center (RTOC) has taken the concept of MSE beyond its traditional approach by developing a Drilling Performance Measure combining data science and statistics to benchmark drilling efficiency. To extract maximum value from the available database, a workflow was developed to construct a Drilling Efficiency Benchmarking Tool. The different steps will be described for performing the data ingestion, cleansing, selection (offset well selection), methodology of computing the statistical model for MSE baseline per Formations and visualization of the output (charts and logs), to compare the actual MSE with baseline and thereby measuring the performance efficiency. The offset wells analysis results show that the workflow can construct an MSE baseline using high frequency data in a meaningful way, which is then set as a target envelope and projected through the real-time platform for monitoring and intervention purposes. This implementation of real-time MSE benchmarking helps identify the area of potential improvement, optimize drilling parameters to ultimately improve ROP and minimize lost time. As an analytical tool, it highlights achievable performance for each field and provide insights to consider new Best Practices.
This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.
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