Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN) approach. The model is designed to provide users, i.e., driller or monitoring specialist, a warning whenever a risk is identified. Since multi-step forecasting is used, the warning is given with enough time for the driller or monitoring specialist to evaluate which preventative action or intervention is necessary. The warnings are provided typically between 30 minutes and 4 hours ahead. The model validation includes the performance metrics and a confusion matrix. Practical cases with real-time wells are also provided. The ML model was proven robust and practical with our data sets, for both historical and live wells. The huge amount of data produced while drilling holds valuable information and when smartly fed into an Artificial Intelligence (AI) model, it can prevent NPT such as stuck pipe events as demonstrated in this paper.
Today's oil and gas drilling operations often face significant technical challenges, especially in remote locations with increasingly difficult geological settings. Stuck pipe incidents have become a major operational challenge for the exploration and production industry, with events typically resulting in substantial amounts of lost time and associated costs. Real-time monitoring has emerged as an important tool to achieve drilling optimization in avoiding downtime, particularly stuck pipe incidents. With the addition of a predictive monitoring system, this process becomes much more effective and competent. Predictive monitoring is used for advanced real-time monitoring in Wells Real Time Center (WRTC) and operational workflows to aid in the drilling execution of complex or critical well sections. The emphasis will be on reducing the complexity of real-time data analysis by utilizing trends and deviations between modelled and actual data to monitor wellbore conditions. This monitoring system and trend-based predictive capability enable drilling teams to detect borehole changes and take preventive action up to several hours in advance. By maximizing productive time, it improves operational efficiency. Predictive monitoring can provide early warning of stuck pipe symptoms, allowing the rig and operations team to take corrective and step-by-step actions. In raw drilling data, the conditions that lead to the stuck pipe can be difficult to read and detect. Various factors may indicate potential problems, but these are frequently missed until the situation has progressed to the point where the drill string becomes stuck. This system could have provided the rig crew with advance notice of changes in downhole conditions, in this case, avoiding the stuck pipe situation. We will look into predictive monitoring adoption in Field B operation as an example. Well E is a highly deviated extended reach well (ERD), with a 12,000ft long horizontal section, exceptionally challenging in terms of geomechanics perspective as well as the well design. When original Well E was drilled, a stuck pipe was encountered which caused the wellbore to be sidetracked. Predictive monitoring was implemented to assist drilling operation for the sidetracked well, and it had been completed successfully with minor hole condition issues. The predictive monitoring system is built around a trio of tightly coupled real-time dynamic models consisting of hydraulic, mechanical, and thermodynamic that simulate the wellbore state and physical processes during drilling operations. These models work together continuously to assess drilling performance, borehole conditions, and any other associated risks. It uses dynamic modelling to accurately model key drilling parameters and variables such as hook load, surface torque, cuttings transport, tank volumes, standpipe pressure, and equivalent circulating density (ECD) in real-time.
With the current rig acceptance workflow practiced by operators globally, the process efficiency gap has been apparent for years. Redundancy, accountability issues and resource wastages can be quite complicated. In a typical workflow, the issues encountered include lack of accountability by inspectors toward item closure, inability to generate snapshots of current status, limited access due to current update via e-mail distribution only, and inefficient process as updates have to be emailed to inspectors. Report formats are not standardized across different disciplines hence the experience is not seamless as there is no one-stop center to view aviation, marine, and HSE inspection items. In fact, some inspection items across disciplines are redundant to each other. The digitalization of rig acceptance workflow can help to overcome these pain points by having a single platform to allow multidiscipline parties to keep tabs on rig activation status and updates throughout company-wide operations globally during the rig acceptance process. The paper approaches the subject by introducing a much leaner and more seamless method for conducting rig acceptance. This can be achieved by having a web-based one-stop center for all things related to rig acceptance (i.e., marine, rig, HSE, and aviation). It grants the ability for inspectors and designated personnel (e.g., DSV) to insert comments for each finding as well as the ability for inspectors to assign and edit severity levels (P1/P2/P3) for each finding. The single platform approach allows the possibility to link up the other checklist and findings on the same system and immediately reduce the redundancy of certain items that is similar to other checklists, which can be streamlined online. Therefore, implementation of this Digital Rig Acceptance Workflow (DRAW) solution can produce a user-friendly online platform to allow inspectors, project teams, management, and rig equipment subject matter experts to access the system anywhere, anytime. DRAW allows status updates (i.e., open/ongoing/close) and clarifications to be communicated via a single platform. It utilizes data input to produce actionable insights (i.e., pie/bar charts, P1/P2/P3 status, etc.) hence generating direct business value via improving process cycle efficiency in a project well life cycle.
The advancement of technology in this era has long profited the oil and gas industry by means of shrinking non-productive time (NPT) events and reducing drilling operational costs via real-time monitoring and intervention. Nevertheless, stuck pipe incidents have been a big concern and pain point for any drilling operations. Real-time monitoring with the aid of dynamic roadmaps of drilling parameters is useful in recognizing potential downhole issues but the initial stuck pipe symptoms are often minuscule in a short time frame hence it is a challenge to identify it in time. Wells Augmented Stuck Pipe Indicator (WASP) is a data-driven method leveraging historical drilling data and auxiliary engineering information to provide an impartial trend detection of impending stuck pipe incidents. WASP is a solution set to tackle the challenge. The solution is anchored on Machine Learning (ML) models which assess real-time drilling data and compute the risk of potential stuck pipe based on drilling activities, probable stuck pipe mechanisms, and operation time. The output of the analysis is built on a warning and alarm system that can be utilized by the engineers to refine and optimize the well construction activities; tackling the stuck pipe issue before it manifests. This solution is evaluated by comparing historical and real-time drilling parameters with the prediction data to generate an error analysis. On top of that, a confusion matrix is tabulated based on the analysis of warnings and alarms raised by the solution to rule out Type 1 and Type 2 errors. The WASP solution has demonstrated tolerably accurate predictions of drilling parameters with minimal warnings and alarms error. With the solution, the stuck pipe issue can be identified hours earlier before the actual stuck pipe was reported in the historical well. It is a powerful tool with the capability to pinpoint possible stuck pipe mechanisms for engineer's immediate analysis and intervention. Value creation from the WASP solution has been massive with a reduction in manhours of analysis, potential NPT events, and unexpected operational costs. Data-driven techniques are effective in preventing stuck pipe incidents and will be scalable to tackle other downhole issues such as loss of circulation, well control, and borehole instability.
The execution phase of the wells technical assurance process is a critical procedure where the drilling operation commences and the well planning program is implemented. During drilling operations, the real-time drilling data are streamed to a real-time centre where it is constantly monitored by a dedicated team of monitoring specialists. If any potential issues or possible opportunities arise, the team will communicate with the operation team on rig for an intervention. This workflow is further enhanced by digital initiatives via big data analytics implementation in PETRONAS. The Digital Standing Instruction to Driller (Digital SID) is a drilling operational procedures documentation tool meant to improve the current process by digitalizing information exchange between office and rig site. Boasting multi-operation usage, it is made fit to context and despite its automated generation, this tool allows flexibility for the operation team to customize the content and more importantly, monitor the execution in real-time. Another tool used in the real-time monitoring platform is the dynamic monitoring drilling system where it allows real-time drilling data to be more intuitive and gives the benefit of foresight. The dynamic nature of the system means that it will update existing roadmaps with extensive real-time data as they come in, hence improving its accuracy as we drill further. Furthermore, an automated drilling key performance indicator (KPI) and performance benchmarking system measures drilling performance to uncover areas of improvement. This will serve as the benchmark for further optimization. On top of that, an artificial intelligence (AI) driven Wells Augmented Stuck Pipe Indicator (WASP) is deployed in the real-time monitoring platform to improve the capability of monitoring specialists to identify stuck pipe symptoms way earlier before the occurrence of the incident. This proactive approach is an improvement to the current process workflow which is less timely and possibly missing the intervention opportunity. These four tools are integrated seamlessly with the real-time monitoring platform hence improving the project management efficiency during the execution phase. The tools are envisioned to offer an agile and efficient process workflow by integrating and tapering down multiple applications in different environments into a single web-based platform which enables better collaboration and faster decision making.
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