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.

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.

Application of Machine Learning Techniques in Reservoir Characterization

2021 ◽  
Author(s):  
Edet Ita Okon ◽  
Dulu Appah
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Learning Algorithm ◽  
Oil And Gas Industry ◽  
Production Performance ◽  
Machine Learning Techniques ◽  
Gas Industry ◽  
Learning Techniques ◽  
Machine Learning Model

Abstract Application of artificial intelligence (AI) and machine learning (ML) is becoming a new addition to the traditional reservoir characterization, petrophysics and monitoring practice in oil and gas industry. Accurate reservoir characterization is the driver to optimize production performance throughout the life of a field. Developing physics-based data models are the key for applying ML techniques to solve complex reservoir problems. The main objective of this study is to apply machine learning techniques in reservoir Characterization. This was achieved via machine learning algorithm using permeability and porosity as the investigative variables. Permeability and porosity of a reservoir were predicted using machine learning technique with the aid of XLSTAT in Excel. The general performance and predictability of the technique as applied to permeability and porosity predictions were compared. From the results obtained, it was observed that the machine learning model used was able to predict about 98% of the permeability and 81% of the porosity. The results from Al and ML will reinforce reservoir engineers to carry out effective reservoir characterization with powerful algorithms based on machine learning techniques. Hence, it can therefore be inferred that machine learning approach has the ability to predict reservoir parameters.

Technology Focus: Data Analytics (October 2021)

2021 ◽  
Vol 73 (10) ◽  
pp. 60-60
Author(s):  
Yagna Oruganti
Keyword(s):  
Machine Learning ◽  
Performance Optimization ◽  
Data Analytics ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Oil And Gas Industry ◽  
Machine Learning Techniques ◽  
Gas Industry ◽  
Learning Techniques ◽  
Reduced Costs

With a moderate- to low-oil-price environment being the new normal, improving process efficiency, thereby leading to hydrocarbon recovery at reduced costs, is becoming the need of the hour. The oil and gas industry generates vast amounts of data that, if properly leveraged, can generate insights that lead to recovering hydrocarbons with reduced costs, better safety records, lower costs associated with equipment downtime, and reduced environmental footprint. Data analytics and machine-learning techniques offer tremendous potential in leveraging the data. An analysis of papers in OnePetro from 2014 to 2020 illustrates the steep increase in the number of machine-learning-related papers year after year. The analysis also reveals reservoir characterization, formation evaluation, and drilling as domains that have seen the highest number of papers on the application of machine-learning techniques. Reservoir characterization in particular is a field that has seen an explosion of papers on machine learning, with the use of convolutional neural networks for fault detection, seismic imaging and inversion, and the use of classical machine-learning algorithms such as random forests for lithofacies classification. Formation evaluation is another area that has gained a lot of traction with applications such as the use of classical machine-learning techniques such as support vector regression to predict rock mechanical properties and the use of deep-learning techniques such as long short-term memory to predict synthetic logs in unconventional reservoirs. Drilling is another domain where a tremendous amount of work has been done with papers on optimizing drilling parameters using techniques such as genetic algorithms, using automated machine-learning frameworks for bit dull grade prediction, and application of natural language processing for stuck-pipe prevention and reduction of nonproductive time. As the application of machine learning toward solving various problems in the upstream oil and gas industry proliferates, explainable artificial intelligence or machine-learning interpretability becomes critical for data scientists and business decision-makers alike. Data scientists need the ability to explain machine-learning models to executives and stakeholders to verify hypotheses and build trust in the models. One of the three highlighted papers used Shapley additive explanations, which is a game-theory-based approach to explain machine-learning outputs, to provide a layer of interpretability to their machine-learning model for identification of identification of geomechanical facies along horizontal wells. A cautionary note: While there is significant promise in applying these techniques, there remain many challenges in capitalizing on the data—lack of common data models in the industry, data silos, data stored in on-premises resources, slow migration of data to the cloud, legacy databases and systems, lack of digitization of older/legacy reports, well logs, and lack of standardization in data-collection methodologies across different facilities and geomarkets, to name a few. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where machine-learning methods have been leveraged. The highlighted papers cover the topics of fatigue dam-age of marine risers and well performance optimization and identification of frackable, brittle, and producible rock along horizontal wells using drilling data. Recommended additional reading at OnePetro: www.onepetro.org. SPE 201597 - Improved Robustness in Long-Term Pressure-Data Analysis Using Wavelets and Deep Learning by Dante Orta Alemán, Stanford University, et al. SPE 202379 - A Network Data Analytics Approach to Assessing Reservoir Uncertainty and Identification of Characteristic Reservoir Models by Eugene Tan, the University of Western Australia, et al. OTC 30936 - Data-Driven Performance Optimization in Section Milling by Shantanu Neema, Chevron, et al.

scholarly journals Industry 4.0 and the Future of Reservoir Exploration and Characterization

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Blanes de Oliveira LA
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Machine Learning Techniques ◽  
Gas Industry ◽  
Production Models ◽  
Wide Range ◽  
Research And Education ◽  
Real Gain

The oil and gas sector seeks to adapt to changes in industry 4.0. Advances in computational processing and artificial intelligence have allowed machines to perform increasingly complex activities. However, the application of these advances to the activities of the oil industry still involves much speculation. While some areas show clear gains with the implementation of machine learning, the exploration and characterization of reservoirs still represent a challenge concerning this topic. As the primary information acquired in reservoirs, such as rock and fluid samples, well logs, and seismic data, presents a wide range of scales, the real gain from machine learning techniques would likely be integrating different databases in different scales. Such integration would improve geological and production models. The spread of information in these databases would also have the potential to decrease exploratory success. The joint efforts of oil and gas companies and research and education institutions will be essential to increase the oil and gas industry.

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.

Implementation of a Pipeline Integrity Management System at TIGF (France): The Added Value of the Pipeline Threats and Mitigations Module

2008 ◽  
Author(s):  
Karine Kutrowski ◽  
Rob Bos ◽  
Jean-Re´gis Piccardino ◽  
Marie Pajot
Keyword(s):  
Management System ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Management Plan ◽  
Point Of View ◽  
Added Value ◽  
Specific Information ◽  
Transmission Grid ◽  
Integrity Management ◽  
Corrective Actions

On January 4th 2007 TIGF published the following invitation for tenders: “Development and Provision of a Pipeline Integrity Management System”. The project was awarded to Bureau Veritas (BV), who proposed to meet the requirements of TIGF with the Threats and Mitigations module of the PiMSlider® suite extended with some customized components. The key features of the PiMSlider® suite are: • More than only IT: a real integrity philosophy, • A simple intuitive tool to store, display and update pipeline data, • Intelligent search utilities to locate specific information about the pipeline and its surrounding, • A scalable application, with a potentially unlimited number of users, • Supervision (during and after implementation) by experienced people from the oil and gas industry. This paper first introduces TIGF and the consortium BV – ATP. It explains in a few words the PIMS philosophy captured in the PiMSlider® suite and focuses on the added value of the pipeline Threats and Mitigations module. Using this module allows the integrity analyst to: • Prioritize pipeline segments for integrity surveillance purposes, • Determine most effective corrective actions, • Assess the benefits of corrective actions by means of what-if scenarios, • Produce a qualitative threats assessment for further use in the integrity management plan, • Optimize integrity aspects from a design, maintenance and operational point of view, • Investigate the influence of different design criteria for pipeline segments. To conclude, TIGF presents the benefits of the tool for their Integrity Management department and for planning inspection and for better knowledge of their gas transmission grid.

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.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

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