Technology Focus: Offshore Drilling and Completion (October 2021)

2021 ◽  
Vol 73 (10) ◽  
pp. 45-45
Author(s):  
Martin Rylance
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Oil And Gas ◽  
Hybrid Approach ◽  
Data Communication ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Real Time Processing ◽  
National University ◽  
Seoul National University

Communication and prediction are symmetrical. Communication, in effect, is prediction about what has happened. And prediction is communication about what is going to happen. Few industries contain as many phases, steps, and levels of interface between the start and end product as the oil and gas industry—field, office, offshore, plant, subsea, downhole, not to mention the disciplinary, functional, managerial, logistics handovers, and boundaries that exist. It therefore is hardly surprising that communication, in all its varied forms, is at the very heart of our business. The papers selected this month demonstrate how improved communication can deliver the prediction required for a variety of reasons, including safety, efficiency, and informational purposes. The application of new and exciting ways of working, partially accelerated by recent events, is leading to breakthrough improvements on all levels. Real-time processing, improved visualization, and predictive and machine-learning methods, as well as improvements in all forms of data communication, are all contributing to incremental enhancements across the board. This month, I encourage the reader to review the selected articles and determine where and how the communication and prediction are occurring and what they are delivering. Then perhaps consider performing an exercise wherein your own day-to-day roles—your own areas of communication, interfacing, and cooperation—are reviewed to see what enhancements you can make as an individual. You may be pleasantly surprised that some simple tweaks to your communication style, frequency, and format can deliver quick wins. In an era of remote working for many individuals, it is an exercise that has some value. Recommended additional reading at OnePetro: www.onepetro.org. OTC 30184 - Augmented Machine-Learning Approach of Rate-of-Penetration Prediction for North Sea Oil Field by Youngjun Hong, Seoul National University, et al. OTC 31278 - A Digital Twin for Real-Time Drilling Hydraulics Simulation Using a Hybrid Approach of Physics and Machine Learning by Prasanna Amur Varadarajan, Schlumberger, et al. OTC 31092 - Integrated Underreamer Technology With Real-Time Communication Helped Eliminate Rathole in Exploratory Operation Offshore Nigeria by Raphael Chidiogo Ozioko, Baker Hughes, et al.

Digital Oil Field; The NPDC Experience

2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Lukman Lawal ◽  
Onyekachi Ike ◽  
Raymond Olugbade ◽  
Fanen Gbuku ◽  
...  
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Data Gathering ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Battery Life ◽  
Gas Industry ◽  
Field Management ◽  
Digital Oilfield ◽  
Data Analytic

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.

scholarly journals Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS

2013 ◽  
Vol 6 (5) ◽  
pp. 8543-8588 ◽  
Author(s):  
A. K. Thorpe ◽  
C. Frankenberg ◽  
D. A. Roberts
Keyword(s):  
Los Angeles ◽  
Spectral Resolution ◽  
Oil And Gas ◽  
Infrared Imaging ◽  
Hybrid Approach ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Optical Absorption Spectroscopy ◽  
Maximum Enhancement ◽  
Near Surface

Abstract. Two quantitative retrieval techniques were evaluated to estimate methane (CH4) enhancement in concentrated plumes using high spatial and moderate spectral resolution data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). An Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm performed well for an ocean scene containing natural CH4 emissions from the Coal Oil Point (COP) seep field near Santa Barbara, California. IMAP-DOAS retrieval precision errors are expected to equal between 0.31 to 0.61 ppm CH4 over the lowest atmospheric layer (height up to 1.04 km), corresponding to about a 30 to 60 ppm error for a 10 m thick plume. However, IMAP-DOAS results for a terrestrial scene were adveresly influenced by the underlying landcover. A hybrid approach using Singular Value Decomposition (SVD) was particularly effective for terrestrial surfaces because it could better account for spectral variability in surface reflectance. Using this approach, a CH4 plume was observed immediately downwind of two hydrocarbon storage tanks at the Inglewood Oil Field in Los Angeles, California, with a maximum near surface enhancement of 8.45 ppm above background. At COP, the distinct plume had a maximum enhancement of 2.85 ppm CH4 above background and was consistent with known seep locations and local wind direction. A sensitivity analysis also indicates CH4 sensitivity should be more than doubled for the next generation AVIRIS sensor (AVIRISng) due to improved spectral resolution and sampling. AVIRIS-like sensors offer the potential to better constrain emissions on local and regional scales, including sources of increasing concern like industrial point source emissions and fugitive CH4 from the oil and gas industry.

scholarly journals Assessment of machine-learning techniques in predicting lithofluid facies logs in hydrocarbon wells

2019 ◽  
Vol 7 (3) ◽  
pp. SF1-SF13 ◽  
Author(s):  
Saba Keynejad ◽  
Marc L. Sbar ◽  
Roy A. Johnson
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Hybrid Approach ◽  
Oil And Gas Industry ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Technical Errors ◽  
Production Stages ◽  
Log Interpretation

Wireline log interpretation is a well-exercised procedure in the oil and gas industry with all its added value from exploration to production stages. It becomes even more important when it is one of only a few available alternatives to compensate for the lack of core samples in a study of lithologic and fluid variations in a well. Yet, as with other purely expert-oriented interpretational techniques, there is always a considerable risk of subjective or technical errors. We have adopted a hybrid approach that links a machine-learning (ML) algorithm to the log interpretation procedure to solve these problems. We have applied this approach to two different hydrocarbon (HC) fields with the aim of predicting the HC-bearing units in the form of lithofluid facies logs at different well locations. The values of these logs are labels of classes that are separated based on their lithologic and fluid content characteristics. After training different MLs on the designed lithofluid facies logs, we chose a bagged-tree algorithm to predict these logs for the target wells due to its superior performance. This algorithm predicted HC units in an accurate interval (above the HC-fluid contact depth), and it showed a very low false discovery rate. The high-accuracy rate, speed of analysis, and its generalization ability, even in data-deficient cases, accentuate why including ML algorithms can improve the understanding of the subsurface at every phase of the exploration and production process. The proposed approach of using ML algorithms, trained and tuned based on the expert’s knowledge of the reservoir, can be modified and applied to future wells in a HC field to significantly minimize the risk of false HC discoveries.

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

scholarly journals Retrieval techniques for airborne imaging of methane concentrations using high spatial and moderate spectral resolution: application to AVIRIS

2014 ◽  
Vol 7 (2) ◽  
pp. 491-506 ◽  
Author(s):  
A. K. Thorpe ◽  
C. Frankenberg ◽  
D. A. Roberts
Keyword(s):  
Los Angeles ◽  
Spectral Resolution ◽  
Oil And Gas ◽  
Infrared Imaging ◽  
Hybrid Approach ◽  
Oil And Gas Industry ◽  
Oil Field ◽  
Optical Absorption Spectroscopy ◽  
Maximum Enhancement ◽  
Near Surface

Abstract. Two quantitative retrieval techniques were evaluated to estimate methane (CH4) enhancement in concentrated plumes using high spatial and moderate spectral resolution data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). An iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm performed well for an ocean scene containing natural CH4 emissions from the Coal Oil Point (COP) seep field near Santa Barbara, California. IMAP-DOAS retrieval precision errors are expected to equal between 0.31 to 0.61 ppm CH4 over the lowest atmospheric layer (height up to 1.04 km), corresponding to about a 30 to 60 ppm error for a 10 m thick plume. However, IMAP-DOAS results for a terrestrial scene were adversely influenced by the underlying land cover. A hybrid approach using singular value decomposition (SVD) was particularly effective for terrestrial surfaces because it could better account for spectral variability in surface reflectance. Using this approach, a CH4 plume was observed extending 0.1 km downwind of two hydrocarbon storage tanks at the Inglewood Oil Field in Los Angeles, California (USA) with a maximum near surface enhancement of 8.45 ppm above background. At COP, the distinct plume had a maximum enhancement of 2.85 ppm CH4 above background, and extended more than 1 km downwind of known seep locations. A sensitivity analysis also indicates CH4 sensitivity should be more than doubled for the next generation AVIRIS sensor (AVIRISng) due to improved spectral resolution and sampling. AVIRIS-like sensors offer the potential to better constrain emissions on local and regional scales, including sources of increasing concern like industrial point source emissions and fugitive CH4 from the oil and gas industry.

scholarly journals Development of Real-Time Time Gated Digital (TGD) OFDR Method and Its Performance Verification

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4865
Author(s):  
Kinzo Kishida ◽  
Artur Guzik ◽  
Ken’ichi Nishiguchi ◽  
Che-Hsien Li ◽  
Daiji Azuma ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Fibers ◽  
High Speed ◽  
Oil And Gas ◽  
Scattered Light ◽  
Oil And Gas Industry ◽  
High Signal ◽  
Chirp Pulse ◽  
Sound Waves ◽  
Industry Applications

Distributed acoustic sensing (DAS) in optical fibers detect dynamic strains or sound waves by measuring the phase or amplitude changes of the scattered light. This contrasts with other distributed (and more conventional) methods, such as distributed temperature (DTS) or strain (DSS), which measure quasi-static physical quantities, such as intensity spectrum of the scattered light. DAS is attracting considerable attention as it complements the conventional distributed measurements. To implement DAS in commercial applications, it is necessary to ensure a sufficiently high signal-noise ratio (SNR) for scattered light detection, suppress its deterioration along the sensing fiber, achieve lower noise floor for weak signals and, moreover, perform high-speed processing within milliseconds (or sometimes even less). In this paper, we present a new, real-time DAS, realized by using the time gated digital-optical frequency domain reflectometry (TGD-OFDR) method, in which the chirp pulse is divided into overlapping bands and assembled after digital decoding. The developed prototype NBX-S4000 generates a chirp signal with a pulse duration of 2 μs and uses a frequency sweep of 100 MHz at a repeating frequency of up to 5 kHz. It allows one to detect sound waves at an 80 km fiber distance range with spatial resolution better than a theoretically calculated value of 2.8 m in real time. The developed prototype was tested in the field in various applications, from earthquake detection and submarine cable sensing to oil and gas industry applications. All obtained results confirmed effectiveness of the method and performance, surpassing, in conventional SM fiber, other commercially available interrogators.

scholarly journals A Literature Review of Hybrid System Dynamics and Agent-Based Modeling in a Produced Water Management Context

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 224-239
Author(s):  
Saeed P. Langarudi ◽  
Robert P. Sabie ◽  
Babak Bahaddin ◽  
Alexander G. Fernald
Keyword(s):  
Water Management ◽  
New Mexico ◽  
Literature Review ◽  
System Dynamics ◽  
Oil And Gas ◽  
Produced Water ◽  
Hybrid Approach ◽  
Oil And Gas Industry ◽  
Hybrid Modeling ◽  
Agent Based

This paper explores the possibility and plausibility of developing a hybrid simulation method combining agent-based (AB) and system dynamics (SD) modeling to address the case study of produced water management (PWM). In southeastern New Mexico, the oil and gas industry generates large volumes of produced water, while at the same time, freshwater resources are scarce. Single-method models are unable to capture the dynamic impacts of PWM on the water budget at both the local and regional levels, hence the need for a more complex hybrid approach. We used the literature, information characterizing produced water in New Mexico, and our preliminary interviews with subject matter experts to develop this framework. We then conducted a systematic literature review to summarize state-of-the-art of hybrid modeling methodologies and techniques. Our research revealed that there is a small but growing volume of hybrid modeling research that could provide some foundational support for modelers interested in hybrid modeling approaches for complex natural resource management issues. We categorized these efforts into four classes based on their approaches to hybrid modeling. It appears that, among these classes, PWM requires the most sophisticated approach, indicating that PWM modelers will need to face serious challenges and break new ground in this realm.

Agile framework for capital projects

2021 ◽  
Vol 61 (2) ◽  
pp. 422
Author(s):  
Polly Mahapatra ◽  
Paris Shahriari
Keyword(s):  
Oil And Gas ◽  
Hybrid Approach ◽  
Oil And Gas Industry ◽  
Quick Response ◽  
Capital Projects ◽  
Gas Industry ◽  
Response To Change ◽  
The Cost ◽  
Early Phases ◽  
The Ideal

Under the increased pressure of rapidly changing market conditions and disrupting technologies, continuous improvements in efficiency become indispensable for all oil and gas operators. Traditional project management principles in the oil and gas industry employ rigid methods of planning and execution that can sometimes hinder adaptability and a quick response to change. Considering the potential that Agile principles can offer as a solution, the challenge, therefore, is to identify the ideal, hybrid, approach that leverages Agile while incorporating the traditional linear workflow necessitated by the oil and gas industry. This paper seeks to assess pre-existing literature in the application of the Agile principles in the oil and gas industry with a focus on Major Capital Projects (MCPs), backed by the successes experienced as a result of specific pilot projects completed at Chevron’s Australian Business Unit. In particular, this paper will focus on how agility has resulted in improvements to the cost, schedule, teaming and cohesion of MCPs in the early phases as well as key learnings form the pilot agility projects.

Study the effect of casting temperature on details from thermo-plastic materials

2020 ◽  
pp. 42-45
Author(s):  
J.A. Kerimov ◽  
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Mold Temperature ◽  
Quality Parameters ◽  
Oil Field ◽  
Casting Temperature ◽  
Gas Industry ◽  
Field Equipment ◽  
Wide Range ◽  
The Impact

The implementation of plastic details in various constructions enables to reduce the prime cost and labor intensity of machine and device manufacturing, decrease the weight of design and improve their quality and reliability at the same time. The studies were carried out with the aim of labor productivity increase and substitution of colored and black metals with plastic masses. For this purpose, the details with certain characteristics were selected for further implementation of developed technological process in oil-gas industry. The paper investigates the impact of cylinder and compression mold temperature on the quality parameters (shrinkage and hardness) of plastic details in oil-field equipment. The accessible boundaries of quality indicators of the details operated in the equipment of exploration, drilling and exploitation of oil and gas industry are studied in a wide range of mode parameters. The mathematic dependences between quality parameters (shrinkage and hardness) of the details on casting temperature are specified.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

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