Content of PetroWiki is intended for personal use only and to supplement, not replace, engineering judgment. SPE disclaims any and all liability for your use of such content. In a seismic context, density contrast is the density of one rock relative to another. The contrast can be positive or negative. Gravity anomalies within sedimentary sections can be analyzed as structural or lithologic anomalies.
Rabinovich, Michael (BP) | Bergeron, John (BP) | Cedillo, Gerardo (BP) | Mousavi, Maryam (BP) | Pineda, Wilson (BP) | Soza, Eric (BP) | Le, Fei (Baker Hughes, a GE Company) | Maurer, Hans-Martin (Baker Hughes, a GE Company) | Mirto, Ettore (Schlumberger) | Sun, Keli (Schlumberger)
Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. Annual Logging Symposium held in The Woodlands, TX, USA June 17-19, 2019. ABSTRACT Typically, only conventional logging while drilling (LWD) resistivity and gamma ray logs are acquired in overburden sections of deep-water wells. Very important decisions impacting drilling safety and borehole stability must be made based on correct and timely interpretation of these logs. Drilling-induced fractures, faults, and eccentricity effects in large holes drilled with oil-based mud are common reasons for anomalous responses of LWD resistivity tools in overburden sections. These anomalies are often associated with fluid losses and other drilling hazards such as borehole assembly sticking. With the limited number of real-time (RT) measurements even if the optimal minimal set of RT curves is selected, the interpretation of these anomalies is challenging. Drilling-induced fractures can be misinterpreted as eccentricity or even as a permeable zone with resistive invasion in water sands or with a hydrocarbon-bearing layer, which is especially important for proper casing and cementing decisions. Resistivity modelling is an irreplaceable tool that enables us to uniquely identify the cause of each anomaly. Time-lapse measurements also help to recognize and identify the causes of anomalies as borehole conditions change with time. Fractures can become deeper with continued overbalance or healed with lost-circulation material or a reduction of equivalent circulating density. Washouts typically enlarge with time and after reaming. We present several case studies from deep-water wells in the Gulf of Mexico illustrating typical LWD resistivity anomalies in overburden sections. The examples include fault identification and borehole events such as fluid losses, borehole enlargement, and gas-bearing intervals. The challenges of interpreting each anomaly and the necessity of the appropriate LWD resistivity modeling kit are clearly demonstrated. Many of the examples illustrate the advantages of measuring after drilling (MAD pass) logs. INTRODUCTION When drilling overburden sections in deep water wells, the hole diameters are big, open hole sections are long and, typically, the LWD suite is limited to conventional resistivity and gamma ray (GR) logs. Additionally, the limited number of real-time (RT) resistivity curves makes the unique interpretation of resistivity data difficult.
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.
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.
In line Inspection (ILI) Interval are often based on conditions that are assumed constant over long sections of pipeline - perhaps entire pipeline systems. Many pipeline operators are following the fixed ILI Interval based on statuary requirement irrespective of different local corrosion growth conditions prevailing on the particular pipeline system. Scheduling the ILI based on maximum interval defined in statuary requirement may be very unrealistic and pose threats to the integrity of these pipelines. This technical paper discusses the importance of ILI Interval, corrosion growth rate analysis, recent development to determine the ILI Interval, an engineering approach to calculate appropriate ILI-RunInterval, mitigation plan to extend the ILI-RunInterval for particular pipeline system. This technical paper would enhance the awareness among the pipeline operators to appropriately calculate the ILI-Run Interval which would cost beneficial to pipeline operators in long term without any integrity threats.
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.
Ng, Sok Mooi (PETRONAS Carigali Sdn. Bhd.) | Khan, Riaz (PETRONAS Carigali Sdn. Bhd.) | Isnadi, Biramarta (PETRONAS Carigali Sdn. Bhd.) | Lee, Luong Ann (PETRONAS Carigali Sdn. Bhd.) | Saminal, Siti Nurshamshinazzatulbalqis (PETRONAS Carigali Sdn. Bhd.)
The objective of this paper is to share the holistic approach to managing aging fleet for offshore fixed steel structures. PETRONAS is currently operating a fleet of more than 200 fixed offshore structures in Malaysian water. More than half of it has exceeded the original design life. With enhanced oil recovery and other developing technologies, offshore platforms often than not are required to continue operating beyond its original design life.
A holistic approach for life extension of fixed offshore structures are being developed to ensure safe operations of the facilities. PETRONAS has started SIM journey since 2007. The approach of Data, Evaluation, Strategy and Programme in line with API RP 2SIM set the basis for managing the integrity of the offshore fleet. An integrated solution was developed to manage both topsides and substructures. The Structural Integrity Compliance System (SICS) which houses the integrity management of topsides deteriorations to prioritize resources through risk based anomalies management. Risk based underwater inspection also formed part of the solutions, addressing mainly extreme storm in the region. Other Major Accidental Hazards (MAHs) risk ranking included in SICS are vessel collision and seismic. Management of facilities with minimal redundancy such as guyed wire monopod is addressed through time based inspection. A regional hazard curve is also developed to ensure the facilities are meeting the acceptance criteria set forth by the industry.
Besides aging, other integrity triggers including shallow gas and subsidence required a different scheme in managing the integrity of the facilities, primarily addressed through a comprehensive monitoring programme.
There is no one size fit all recipe in managing the aged platforms for life extension. The data plays a crucial role in ensuring the right methodology is deployed in support of digitalization and data driven decision making. Implementation of the system is proven to be reliable in ensuring the offshore fixed structures are intact to support safe and continuous operations to the operator in a cost optimum manner. The data analytics help to enhance the predictive model to optimize the inspection and maintenance programme.
Strong ownership and commitment of the structural integrity engineers in ensuring the data integrity maintain the challenge in sustainability of the system and provide reliable source for data driven decision making to the operator.
Category: Operational Excellence (136 - Managing Aging Facilities)
Imrie, Andrew (Halliburton Energy Services) | Negenman, Brendon (Halliburton Energy Services) | Lee, Chung Yee (Halliburton Energy Services) | Iyer, Mahadevan S. (Halliburton Energy Services) | Parashar, Sarvagya (Halliburton Energy Services) | Shata, Mohamed Raouf (Halliburton Energy Services) | Helton, Sean (ConocoPhillips)
The identification of low-rate leaks along with low annular-pressure buildup rates in any type of completion presents challenges in the well-integrity domain. This paper emphasizes the importance of understanding the well-diagnostic problem to determine feasibility, isolate interest zones, enhance stimulation strategies, and ultimately optimize the acquisition of high-resolution acoustical data from the wellbore with a latest-generation advanced leak-detection tool.
This case study discusses the methodology that underlies the successful determination of the depths and the radial locations in the outer casing strings of multiple leaks in an offshore well. In the study presented, emphasis had been placed on the job planning to provide adequate or substantial leak stimulation for the accurate determination of the leak points in terms of radial distance away from the tool axis within the wellbore. Rather than a shut-in and flowing or venting acquisition, it was proposed that the optimal method for the successful determination of an outer casing string leak involved invoking a range of flow rates and, therefore, acoustic levels, across an extended period. The study also demonstrates the advantages of integrating acoustic-based tools with conventional production logging tools.
Two outer string casing leaks with annulus to formation communication areas were identified from high-resolution leak-detection logging coupled with conventional pressure and temperature measurements. The interpretation process included the computation of a 2D radial map of the flow activity across each zone of interest. This process resulted in less ambiguity and clearer results obtained in real time during the acquisition. The location of each leak point was triangulated using an error-minimization algorithm from the received acoustic waveforms at the tool receiver array. Further, the optimized stimulation strategy enabled leak-stimulation responses to be tracked in the computed power spectral density (PSD) at each leak. This process enabled the operator to promptly move on with the well abandonment strategy without waiting for further data analysis.
Attention to detail from the outset and a complete understanding of the well and its annular pressure and fluid behavior enabled an optimized and focused electric line diagnostic strategy to be used. The use of high-resolution acoustic data from an advanced leak-detection tool with an array of hydrophones ensured that the multiple leak locations were identified and characterized.
The anelastic effects of the earth can cause frequency dependent energy attenuation and phase distortion, especially when gas clouds are present. To correct these unwanted effects for proper imaging, both the velocity and quality factor (Q) models need to be accurately estimated. With FWI offering the capability to obtain higher-resolution models than tomography, visco-acoustic FWI (Q-FWI) is highly desirable for inverting both Q and velocity models together.
The visco-acoustic wave propagation in an anisotropic medium and the gradient computation for model parameters can be implemented in the framework of FWI. However, the similar radiation patterns between velocity and Q make the joint inversion non-trivial (
In recent years, the Drilling and Workover (D&WO) operations are growing significantly. The growth of active operations required and produced more data from D&WO operations. With very large number of rig activities daily transmitting more than 60,000 real-time data points every second, it became necessary to understand and utilize this Big Data in order to predict drilling troubles and discover hidden knowledge. The adaption of the industrial Revolution (IR) 4.0 contributed to the use of advanced and novel approaches such as Artificial intelligence (AI) and Machine learning (ML) models. However, those models require continues improvement as drilling data change. When using the industrial standard and adapted Wellsite Information Transfer Specification Markup Language (WITSML) based Big Data environment, the task to monitor the performance of a model at a large scale becomes challenging due to common reasons such as a large number of wells, different models being deployed and different data stored in different systems.
In this paper, a new approach is introduced using WITSML based Big Data environment. The methods employed utilize an advanced engine to monitor and evaluate active AI/ML models at a large scale. The engine utilizes anomaly detection methods to monitor abnormal behaviors of the models such as sudden high rate of alerts per day/well or a sudden drop in true event detection. The paper will also demonstrate how such technology can help in early detection of model's decay signs or sudden changes in real-time data quality.
The solution improved and automated the process of monitoring and maintaining of AI/ML models in the Drilling domain. It also made the decay detection of models possible and showed how models improve when iterative enhancements are deployed.