In recent years, the oil and gas industry has gained greater operational efficiencies and productivity by deploying advanced technologies, such as smart sensors, data analytics, artificial intelligence and machine learning — all linked via Internet of Things connectivity. This transformation is profound, but just starting. Leading offshore E&P operators envision using such applications to help drive their production costs to as low as $7 per barrel or less. A large North Sea operator among them successfully deployed a low-manned platform in the Ivar Aasen field in December 2016, operating it via redundant control rooms — one on the platform, the other onshore 1,000 kilometers away in Trondheim, Norway. In January 2019, the offshore control room operators handed over the platform's control to the onshore operators, and it is now managed exclusively from the onshore one. One particular application — remote condition monitoring of equipment — supports a proactive, more predictive condition-based maintenance program, which is helping to ensure equipment availability, maximize utilization, and find ways to improve performance. This paper will explain the use case in greater detail, including insights into how artificial intelligence and machine learning are incorporated into this operational model. Also described will be the application of a closed-loop lifecycle platform management model, using the concepts of digital twins from pre-FEED and FEED phases through construction, commissioning, and an expected lifecycle spanning 20 years of operations. It is derived from an update to a paper presented at the 2018 SPE Offshore Technology Conference (OTC) that introduced the use case in its 2017-18 operating model, but that was before the debut of the platform's exclusive monitoring of its operations by its onshore control room.
An operator considered using Constant Bottomhole Pressure (CBHP) Managed Pressure Drilling (MPD) in the evaluation phase of a drilling project and decided not to go forward with MPD. While drilling the well, unfortunately, they had a well control event that required an increased mud-weight ultimately resulting in a differentially stuck-pipe condition.
MPD services were exclusively called to help free this differentially stuck pipe/BHA. MPD provided enough flexibility to deliberately reduce the wellbore pressure below pore-pressure and decrease the differential pressure to free the stuck pipe/BHA. Using CBHP variation of MPD resulted in unsticking the pipe as explained in this case history. The detected influx was circulated out with appropriate pump rate (high flow rate) using MPD equipment. The operator drilled forward with the assistance and additional protection of MPD to reach the Targeted Depth (TD) without having further issues in a very narrow drilling window. This successful field operation shows that CBHP MPD can indeed be used to precisely manage the annular pressures, as elaborated in the IADC’s MPD definition, and safely and successfully solve some of the baffling problems of the drilling industry.
Nauduri, Sagar (Pruitt Optimal) | Parker, Martyn (Pruitt Optimal) | Nabiyev, Akram (Pruitt Optimal) | Sampley, Eddy (Pruitt Optimal) | Kirstein, Lenord (Pruitt Optimal) | Morris, Jason (Pruitt Optimal) | Wilkinson, Matthew (Pruitt Optimal) | Buckner, Jason (Pruitt Optimal)
A novel drilling solution, ‘Constant Bottomhole Pressure (CBHP) Managed Pressure Drilling (MPD) assisted Casing Drilling operation’, was designed, planned and successfully executed for different operators on multiple directional wells in North America. These wells were otherwise not drillable either conventionally or with CBHP MPD using conventional drillpipe-BHA; and over the last few decades several operators tried and failed to reach the Target Depth (TD) on multiple occasions when drilling some of these formations.
One operator drilled in formations prone to severe faulting/fracturing and with very high permeability, while a different operator drilled through multiple weak zones interbedded with over-pressured and highly conductive regions. Both scenarios resulted in similar issues with fluid displacement, tripping/surge and swab, kicks and losses, running casing and cementing. The generic CBHP MPD solution with a conventional drillpipe-BHA even with ‘Anchor Point’ CBHP MPD and its variations was not successful in either of these scenarios in drilling to the TD.
As demonstrated using case histories, the success in these projects was a result of combining two technologies - ‘CBHP MPD’ and ‘Casing Drilling’. Pre-planning, understanding formation constraints, training, and having knowledgeable and experienced people involved, enabled safe and successful execution of CBHP MPD assisted Casing Drilling on these projects and helped CBHP MPD develop and reach new horizons.
The effectiveness of secondary and tertiary recovery projects depends heavily on the operator's understanding of the fluid flow characteristics within the reservoir. 3D geo-cellular models and finite element/difference-based simulators may be used to investigate reservoir dynamics, but the approach generally entails a computationally expensive and time-consuming workflow. This paper presents a workflow that integrates rapid analytical method and data-analytics technique to quickly analyze fluid flow and reservoir characteristics for producing near "real-time" results. This fast-track workflow guides reservoir operations including injection fluid allocation, well performance monitoring, surveillance, and optimization, and delivers solutions to the operator using a website application on a cloud-based environment. This web-based system employs a continuity governing equation (Capacitance Resistance Modelling, CRM) to analyze inter-well communication using only injection and production data. The analytic initially matches production history to determine a potential time response between injectors and producers, and simultaneously calculates the connectivity between each pair of wells. Based on the inter-well relationships described by the connectivity network, the workflow facilitates what-if scenarios. This workflow is suitable to study the impact of different injection plans, constraints, and events on production estimation, performance monitoring, anomaly alerts, flood breakthrough, injection fluid supply, and equipment constraints. The system also allows automatic injection re-design based on different number of injection wells to guide injection allocation and drainage volume management for flood optimization solutions. A field located in the Midland basin was analyzed to optimize flood recovery efficiency and apply surveillance assistance. The unit consists of 11 injectors and 22 producers. After optimization, a solution delivering a 30% incremental oil production over an 18-month period was derived. The analysis also predicted several instances of early water breakthrough and high water cut, and subsequent mitigation options. This system couples established waterflood analytics, CRM and modern data-analytics, with a web-based deliverable to provide operators with near "real-time" surveillance and operational optimizations.
Challenging drilling operations in the Vaca Muerta unconventional shale gas play have prompted operators to implement innovative drilling techniques to improve drillability and operational efficiency. Significant benefits have been reported by utilizing Managed Pressure Drilling (MPD), Underbalanced Drilling (UBD), and/or drilling with casing; however, challenges still exist, due to a variety of reasons. The heterogeneity found from field to field and within fields has resulted in wells with significant events, some resulting in loss of the well, even on the same pad where a previous well has been drilled uneventfully.
Arguably, the most successful non-conventional drilling technique being incrementally used in the area is MPD, often combined with UBD, particularly in gas wells. As with any new technology implementation, there is a learning curve which can be accelerated by translating learnings from successful experiences.
Three key components for a successful implementation on MPD are still building a collective experience in the Vaca Muerta play. Firstly, the equipment and associated technology is the key enabler for physically perform the operations safely and efficiently.
The second component is a ‘soft’ framework consisting of a robust layered approach including overarching standard policies, the MPD strategy for implementation in the specific project, conceptual and detailed procedures, and specific work instructions.
Lastly, the human component is a group of competent personnel, whom, at their specific responsibility level, understand the ‘soft’ framework, and knows how to operate the hardware to implement the technology so that objectives are met.
The potential of the technology is limited to the weakest of these three components. A strong combination of any two of them, not complemented by the third one, will most likely result in a partial success at best, if not a complete failure at worst.
The operator had recently three major events in wells being drilled with MPD, which resulted in the loss of the wells. After implementing a training program on MPD/UBD, which emphasized the human factor and understanding of the equipment, the ‘soft’ framework of strategy, procedures and project management, the safety and efficiency during operations has increased significantly. This resulted in a better handling of events related to bottom hole pressure control without a single well loss event to the date of writing this document, approximately nine months of continuous operations. The other mainstay of this process has been the flexibility to adapt the application of the methodology based on the well challenges encountered.
Plunger lifted, and free-flowing gas wells experience a wide range of issues and operational inefficiencies such as liquid-loading, downhole and surface restrictions, stuck or leaking motor control valves, and metering issues. These issues can lead to extended downtime, equipment failures, and other production inefficiencies. Using data science and machine-learning algorithms, a self-adjusting anomaly detection model considers all sensor data, including the associated statistical behavior and correlations, to parse any underlying issues and anomalies and classifies the potential cause(s). This paper presents the result of a Proof of Concept (PoC) study conducted for a South Texas operator encompassing 50 wells over a three-month period. The results indicate an improvement compared to the operators' visual inspection and surveillance anomaly detection system. The model allows operators to focus their time on solving problems instead of discovering them. This novel approach to anomaly detection improves workflow efficiencies, decreases lease operating expenses (LOE), and increases production by reducing downtime.
Surface Applied Back Pressure Managed Pressure Drilling (MPD) systems provide a potentially game changing technology for Deepwater Gulf of Mexico drilling applications by means of annular pressure manipulation for drilling through narrow margins, cementing across potential loss zones, assisting in running completions, and mitigating nonproductive time. The technology however, is not without cost and the challenge remains to build the business case to utilize MPD in Deepwater applications. Recently several wells were successfully drilled using this technology to the planned target depth accessing reserves that would not have been possible otherwise. This type of scenario where using MPD to stay within a narrow margin has been the means to justify the upfront costs to get a rig outfitted with MPD and the operating costs of the system during use. Once a rig is outfitted with MPD, the economics for a project shift, however, justifying the business case purely based on NPT savings is still not typically viable. This paper will provide the operator's perspective of the cost-benefit analysis for MPD use and provide business case examples for the use in Deepwater Gulf of Mexico. The impact of lessons learned on an ongoing campaign and the savings viable for this and other implementation scenarios will also be discussed to develop a robust case for MPD adoption.
The success of a pilot milling operation is dependent on the mill design, adherence to correct milling parameters and precise location of stabilizing members in the bottomhole assembly (BHA), especially while milling through large casings such as 20 inch inside 30 inch conductor. This paper discusses the importance of correct mill design and stabilization of the BHA, along with field results from milling with under-gauged mill and stabilizers.
Pilot milling interventions to facilitate open-hole side-tracking can be very effective and cost-efficient, especially in cases where retaining the original borehole size is necessary for further workover operations, for example, when liner is milled for this purpose. Pilot milling is a suitable option where sidetracking with a whipstock is not viable, as when casing has collapsed, with internal diameter restrictions, or situations where irreparable surface damage to conductor pipe and casing have occurred due to corrosion. Such situations might result in losing an offshore platform slot, which is a huge cost to operators.
One such situation was encountered where 30 inch conductor pipe parted at the water line due to corrosion. Prolonged exposure to corrosion further led to 20 inch casing parting at the water line as well. Surface repairs were attempted but were unable to arrest annulus leakage. In order to recover the slot, an improved and specially designed pilot mill was used. A stabilized milling bottom-hole assembly with precise sizes and locations of stabilizers was incorporated. This new mill design resulted in milling 585.6 feet of 20 inch casing with an average rate of penetration (ROP) of 2.6 ft/hr. The new mill design resulted in good mill life and only two mill runs were made in the entire milling operation. Results of torque and drag simulations to study the bending stresses and torsional stresses on mill string components while milling are discussed. Catastrophic effects of using under-gauged mill and stabilizers were also examined.
This improved mill and stabilized bottom-hole assembly design offers optimum ROP, improved mill life, reduced surface vibrations and a fine metal cutting structure that eases metal debris handling at surface.
Essam, Wael (BP) | Scarborough, Christopher (BP) | Wilson, Nick (BP) | Shimi, Ahmed (BP) | Santos, Helio (Safekick) | Hannam, Jason (Safekick) | Catak, Erdem (Safekick) | Lancaster, Jay (Seadrill) | Gooding, Neil (Seadrill) | Baan, Robert (Seadrill)
BP had long recognized the benefits of MPD, having been using it for years to deliver very challenging wells in Egypt, Trinidad and the North Sea; and it was time to bring these benefits to its GoM operations. Once the company team identified a portfolio of suitable candidate wells to allow the economics of the application to be advantageous, they partnered with Seadrill to provide the MPD service integrated into the West Capricorn drilling rig. This approach builds on synergies within the drilling contractor organization to achieve long term economic, competency, and risk management benefits, resulting from integrating this drilling method on the rig, and eliminating interfaces with 3rd party providers. The paper will discuss how the company and the drilling contractor teams, together with equipment suppliers and training providers, managed the project from initial system design, to installation and commissioning, to the successful delivery of the first well using MPD, at top quartile performance. It will discuss the process for optimizing the design and testing it from a reliability and process safety perspectives; engaging the regulatory authority and the classification society; integrating MPD in the well planning process and developing operational procedures for use on the rig; and delivering a training program for the wider team covering the technical and the human factors aspects to ensure a successful delivery.
The use of artificial lift equipment for oil production in onshore reservoirs is becoming increasingly more important to help sustain the production rates of declining oil fields. Oil field producers therefore depend on the efficient operation of the artificial lift equipped and it is becoming increasingly more important to ensure maximum uptime of the equipment for the continuous production of oil.
One of the widely used methods for artificial lift is using Electrical Submersible Pumps to produce high volumes from deep oil wells. The ESP is a very effective method of artificial lift due to its unique characteristic of having the complete pump assembly and electrical motor submersed directly in the well fluid. This however requires a complex technical design of the pump and electrical motor to ensure safe operation several thousand feed below surface. It is therefore necessary to implement systems that can monitor the pump operation and notify the operator of events that will result in failure of the equipment.
Typically, ESPs are connected to SCADA or other distributed control systems to provide supervisory and control functions for their effective operation as well as for operational visibility. Today, many diagnostic methods are available to determine the health and status of an ESP system by making use of that functionality in its automation system. However, while these methods can provide insightful analysis of problems, they usually require constant monitoring of a human operator who is able to react in time to alarm notifications or implement corrective action. The correct operation of the ESP largely depends on the decisions made by the ESP field operator and his ability to effectively control the ESP fleet based on his experience. The complexity of the operator’s task increases with the size of the of ESP fleet that the operator must manage at any given point in time.
But this situation is changing, with efforts being made to reduce the dependency on the human operator by implementing digital support systems. With recent advances in artificial intelligence (AI) combined with the new Internet of Things (IoT) technologies, it is possible to effectively use data-driven analytics fueled by large data sets to assist the operator with the task of operating ESP fleets. In particular, AI technology that involves deep learning and neural networks can be extremely effective in detecting abnormal behavior of complex physical systems such as ESPs, based on the data gathered from the system, providing decision support for remediating or managing the causative issues.
One of the primary advantages of using AI technology is its ability to detect abnormal behavior in complex systems. Such an AI system can be implemented to monitor ESP systems using the real-time process data from the Supervisory system and then using a neural network model identify abnormal ESP pump behavior. The paper discuss how such an AI based anomaly detection systems can be used in a extended form to implemented an autonomous surveillance system which can monitor and entire ESP fleet. The purpose of the autonomous surveillance system is to support the operator in his supervisory tasks by doing the selection and prioritization of ESP units that requires operator attention.
This paper is a continuation of an earlier paper which discussed the possibility to implement a predictive maintenance system for ESPs using AI. This paper further elaborates the implementation of an autonomous surveillance solution for ESP systems using the predictive maintenance solution and explain how it can be implemented using AI technology in combination with a cloud-based IoT platform.