Technology Focus: Data Analytics (October 2021)

2021 ◽  
Vol 73 (10) ◽  
pp. 60-60
Author(s):  
Yagna Oruganti

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.

2021 ◽  
Author(s):  
Edet Ita Okon ◽  
Dulu Appah

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.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Blanes de Oliveira LA

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.


Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.


2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.


2021 ◽  
pp. 1-13
Author(s):  
Shantanu Chakraborty ◽  
Samit Mondal ◽  
Rima Chatterjee

Summary Fluid-replacement modeling (FRM) is a fundamental step in rock physics scenario modeling. The results help to conduct forward modeling for prediction of seismic signatures. Further, the analysis of the results improves the accuracy of quantitative interpretation and leads to an updated reservoir characterization. While modeling for different possible reservoir pore fluid scenarios, the quality of the results largely depends on the accuracy of the FRM. Gassmann (1951)fluid-replacement modeling (GFRM) is one of the widely adopted methods across the oil and gas industry. However, the Gassmann method assumes the reservoir as clean sandstone with connected pores. This causes Gassmann fluid-replacement results to overestimate the fluid effect in shaly sandstones. This study uses neutron and density logs to correct the overestimated results in shaly sandstone reservoirs. Due to the nature of these recordings, both of these log readings have close dependencies on the presence of shale. When the logs are plotted in a justified scale, the differences between the logs provide an accurate measurement of shaliness within the reservoir. The study has formulated a weight factor using the logs, which has further been used to scale the overestimated Gassmann-modeled fluid effect. The results of the revised method are independent of type of clay presence and associated effective porosity. Moreover, the corrected FRM results from the revised Gassmann method shows good agreement with rock physical interpretation of shaly sandstone reservoirs.


Sign in / Sign up

Export Citation Format

Share Document