Multivariate Sensitivity Analysis and Real-Time Algorithms in Data-Driven Coiled Tubing Cleanouts Turn Insights into Action
Abstract: Depleted wells require underbalanced coiled tubing cleanouts (CTCO) in which natural production from the reservoir assists solids transport. Reservoir pressures are often uncertain in these subhydrostatic environments, making CTCO design conditions difficult to predict. Under these conditions, sustaining an efficient cleanout is challenging, and risks include undesired leakoff, damage to the wellbore, and stuck pipe. New physics-based algorithms and workflows consume real-time data and output actionable feedba… Show more
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Cloud-Based Planning and Real-Time Algorithms Improve Coiled Tubing Cleanout Efficiency
Depleted wells require underbalanced coiled tubing cleanouts (CTCO) in which natural production from the reservoir assists solids transport. Conventional cleanout methods relying on fluid circulation pose a risk of fluid loss, reducing annular velocity and increasing the risk of formation damage or stuck CT pipe incidents. The use of nitrified fluids addresses some of those risks, but also introduces a new set of challenges. In addition to technical challenges, cleanout operations face logistics and operational constraints, which directly impact the feasibility and viability of the intervention. Digital tools provide a path toward increased efficiency and success rate of CTCO, but the suite of legacy software often used in CT operations relies on monolithic implementations, which limit the possible optimization of the planning and the connection between design and execution data. More generally, reliance on manual operations (whether during the design or execution phases) often leads to missing on potential optimization opportunities. The transformation of CTCO leveraging a new cloud-based CT hydraulics (CTH) simulator, real-time execution advisors, and autonomous conveyance brings a new level of flexibility and interconnectivity to the design and execution phases. CTH features state-of-the-art flow and transport models, which improve CTCO design capabilities, providing the required insights during execution time to optimize the cleanout operation. During the design phase of underbalanced CTCO, the designer needs to work with uncertainty on several parameters, such as reservoir pressure or PI distribution of the horizontal section. The architecture of the CTH allows sensitizing over every parameter, which generates a combinatorial number of scenarios, driving a larger-than-usual processing demand. The cloud-based service's processing capacity meets these demands during the job design phase to generate a large database of sensitized scenarios and delineate a safe and effective operational envelope. Two case studies show how CTH can be used during the design phase to ensure more efficient job execution in two horizontal oil wells in the Valhall brownfield. In the first one, the simulator was used to guarantee that the cleanout execution would be possible even if contingency plans due to gas lift valve failure had to be triggered. In the second, sensitivity analysis was conducted over the pumping rate and formation pressure, identifying a safe operating envelope that, once coupled with an adequate execution approach, led to 20% oil base savings. Efficiency of CTCO operations is further improved by implementing autonomous conveyance execution during the operations. This includes automatic control of depth and speed, achieving more than 10% more efficient speeds during run-in-hole and pull-out-of-hole activities. Pull tests need to be performed at set intervals during conveyance to ensure that the pipe is not getting stuck, which accelerates fatigue of the CT pipe. The autonomous system also includes a pull test optimizer that accounts for the pipe's fatigue profile, weld locations, and completion data to strategically adjust the pull test schedule. This reduces the effect of these tests on pipe fatigue by up to 28% over its lifetime and lowers the risks linked with running across downhole restrictions. Besides, autonomous conveyance and pull test execution liberates the CT operator to concentrate on other crucial aspects of the operation. These include managing and monitoring the CT unit, fluid pumps, remote-operated choke, and downhole tools, controlling real-time parameters, updating the job log, and managing the crew. This study demonstrates that by combining extensive cloud computing, advanced flow models, surface and downhole measurements with real-time interpretation and inference algorithms, and autonomous operations, CTCO operations can be conducted safer and more efficiently, in a repeatable manner, therefore reducing the operating time, fluid pumped, pipe fatigue, and greenhouse emissions, and allowing to raise the success rate of those operations to a new industry benchmark level.
Cloud-Based Planning and Real-Time Algorithms Improve Coiled Tubing Cleanout Efficiency
Depleted wells require underbalanced coiled tubing cleanouts (CTCO) in which natural production from the reservoir assists solids transport. Conventional cleanout methods relying on fluid circulation pose a risk of fluid loss, reducing annular velocity and increasing the risk of formation damage or stuck CT pipe incidents. The use of nitrified fluids addresses some of those risks, but also introduces a new set of challenges. In addition to technical challenges, cleanout operations face logistics and operational constraints, which directly impact the feasibility and viability of the intervention. Digital tools provide a path toward increased efficiency and success rate of CTCO, but the suite of legacy software often used in CT operations relies on monolithic implementations, which limit the possible optimization of the planning and the connection between design and execution data. More generally, reliance on manual operations (whether during the design or execution phases) often leads to missing on potential optimization opportunities. The transformation of CTCO leveraging a new cloud-based CT hydraulics (CTH) simulator, real-time execution advisors, and autonomous conveyance brings a new level of flexibility and interconnectivity to the design and execution phases. CTH features state-of-the-art flow and transport models, which improve CTCO design capabilities, providing the required insights during execution time to optimize the cleanout operation. During the design phase of underbalanced CTCO, the designer needs to work with uncertainty on several parameters, such as reservoir pressure or PI distribution of the horizontal section. The architecture of the CTH allows sensitizing over every parameter, which generates a combinatorial number of scenarios, driving a larger-than-usual processing demand. The cloud-based service's processing capacity meets these demands during the job design phase to generate a large database of sensitized scenarios and delineate a safe and effective operational envelope. Two case studies show how CTH can be used during the design phase to ensure more efficient job execution in two horizontal oil wells in the Valhall brownfield. In the first one, the simulator was used to guarantee that the cleanout execution would be possible even if contingency plans due to gas lift valve failure had to be triggered. In the second, sensitivity analysis was conducted over the pumping rate and formation pressure, identifying a safe operating envelope that, once coupled with an adequate execution approach, led to 20% oil base savings. Efficiency of CTCO operations is further improved by implementing autonomous conveyance execution during the operations. This includes automatic control of depth and speed, achieving more than 10% more efficient speeds during run-in-hole and pull-out-of-hole activities. Pull tests need to be performed at set intervals during conveyance to ensure that the pipe is not getting stuck, which accelerates fatigue of the CT pipe. The autonomous system also includes a pull test optimizer that accounts for the pipe's fatigue profile, weld locations, and completion data to strategically adjust the pull test schedule. This reduces the effect of these tests on pipe fatigue by up to 28% over its lifetime and lowers the risks linked with running across downhole restrictions. Besides, autonomous conveyance and pull test execution liberates the CT operator to concentrate on other crucial aspects of the operation. These include managing and monitoring the CT unit, fluid pumps, remote-operated choke, and downhole tools, controlling real-time parameters, updating the job log, and managing the crew. This study demonstrates that by combining extensive cloud computing, advanced flow models, surface and downhole measurements with real-time interpretation and inference algorithms, and autonomous operations, CTCO operations can be conducted safer and more efficiently, in a repeatable manner, therefore reducing the operating time, fluid pumped, pipe fatigue, and greenhouse emissions, and allowing to raise the success rate of those operations to a new industry benchmark level.
New Coiled Tubing Fatigue Management System with Real-Time Vessel Data: Step-Change to Cost and Reliability in Open-Water Interventions
Early attempts to monitor coiled tubing (CT) fatigue in open-water interventions relied on basic statistical methods with a constant damage rate and broad generalizations of sea conditions. The new CT fatigue management system (FMS) comprises hardware and software that improve fatigue tracking. It tracks high-cycle fatigue (HCF) and low-cycle fatigue (LCF) simultaneously and in real time, elevating the accuracy, immediacy, and reliability of CT fatigue monitoring in open-water interventions. The hardware monitors vessel pitch and roll in real time and relays the data to an acquisition system where they are combined with CT depth and circulating pressure. The new acquisition software consumes these data as well as a CT string fatigue life database and geometry and positions of the CT string, work drum, and payout sheave. LCF and HCF are accurately modeled, and the string fatigue life is updated in real time. That information allows the operation to strategically cycle the CT pipe to manage fatigue more efficiently and extend the time before a trim is necessary. The traditional statistical method recorded a cumulative CT fatigue damage of 16% over a 14-hour job. In that same timeframe, the CT FMS measured a cumulative fatigue of 3.8%. Since the CT FMS updates damage rate continuously in response to vessel dynamics and uses a nonlinear methodology to combine HCF and LCF, it reduces inaccuracy and eliminates unnecessary safety factors associated with the statistical method. By improving precision and reliability, the real-time CT FMS safely extended the useful life of CT pipes by 75%. The CT FMS also improves operational integrity by introducing alarm threshold settings and custom material parameters. Alarms are raised reflecting fatigue life growth, tubing payout, and changes to sea conditions. This provides the operator with real-time, actionable data to ensure a more even distribution of the fatigue along the CT. Besides extending useful life, this reduces the risk of CT failure, which can lead to costly downtime, reputational damage, and production losses. The string's operating limits are more accurately monitored, which extends the capacity of the CT into harsher environments, longer operations, and riserless interventions of wider workscope. The CT FMS automates workflows that previously required a dedicated technician, reducing crew size on board. The introduction of CT in open-water interventions brought with it the risk of CT failure, which is inherent to any operation that handles CT pipe. This industry first, real-time CT FMS combines HCF and LCF monitoring and is a step change from the traditional methods to address these CT-related risks. Furthermore, the CT FMS elevates overall operational integrity, increases operational efficiency, and extends the operational envelope and application of CT in open-water interventions.
Labeling Job Type and Technology for Large Operational Datasets
Each year, coiled tubing (CT) well intervention fleets produce terabytes of multimodal data; these are recorded from surface and downhole sensors on each job. Among these data are job type(s) and technologies used on each job; traditionally, a field crew manually supplies this information. Given the diversity of data, many acquisition labels are often missing or inaccurate. A multimodal framework is presented that automatically identifies the job type and technologies used during an acquisition. The proposed framework leverages different types of data depending on the job and technology to identify. Most job types (e.g., CT milling, CT fishing), and general technologies (e.g., downhole telemetry, jetting nozzles), are identified through a natural language processing (NLP) algorithm applied to operational reports. The presence of downhole technologies with greater granularity and certainty (e.g., downhole 2 1/8-in. pressure and temperature sonde) lies in the detection of meaningful information or noise on specific channels and follows a logic mimicking human interpretation. Finally, CT cementing and electronic firing heads are identified through statistical metrics and pattern recognition. The framework leverages several tested methods. Primary job types and general technologies are classified using NLP. Of 366 acquisitions in the cloud archival system, the algorithm labels 97% with job type and 26% with one or more technologies. The second method extends the resolution and number of detected technologies to cover 12 unique real-time telemetry modules such as pressure, temperature, casing collar locator, and gamma ray sondes. For this method, over 50 acquisitions are analyzed with an accuracy of 94%. Electronic firing head signatures for three unique types of firing heads are identified successfully 97% of the time on 44 different acquisitions. This identification is sped up by automatically identifying three major stages of a CT well intervention (i.e., initial run in hole, service delivery, and final pull out of hole) and restricting the search space to the relevant stage. CT cementing job type identification is tested on 52 different jobs, with an accuracy of 92% and less than 30% of false negatives. The innovative framework automatically classifies jobs and technologies. The inherent methods combine domain knowledge with the power of machine learning to enable efficient mining of data that would otherwise remain out of reach. By automating labeling, human error is largely eliminated and the reliability of the contextual metadata is significantly improved to provide crucial insights regarding operations.
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