Data-Driven Production Forecasting of Unconventional Wells with Apache Spark

Mohammadmoradi, Peyman; Moradi, Hessam M.; Kantzas, Apostolos

doi:10.2118/190098-ms

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…2) LEADING COUNTRIES Figure 3(d) presents the paper distribution in terms of nationality and region. Notably, 16% of papers are from international projects involving many countries, such as [15], [63], [64]. This feature indicates the critical role of international research collaborations in developing BD technologies.…”

Section: Review Findings and Discussionmentioning

confidence: 99%

“…In O&G applications, many projects attempt to replace Hadoop with Spark for faster analysis of tasks such as industrial alarm management [104], seismic data analysis [105], production forecasting of unconventional wells [64], and well casing damage prevention [41]. Remarkably, for gas lift well surveillance, Spark has been employed to develop new online realtime visual analytics of distributed temperature sensor measurements [106].…”

Section: ) Apache Sparkmentioning

confidence: 99%

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A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

2020

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Big data (BD) analytics is one of the critical components in the digitalization of the oil and gas (O&G) industry. Its focus is managing and processing a high volume of data to improve operational efficiency, enhance decision making and mitigate risks in the workplace. Enhanced processing of seismic data also provides the industry with a better understanding of BD applications. However, the industry still exercises caution in adopting new technologies. The slow pace of technology adoption can be attributed to various causes, from the obstacles to the integration with existing systems, to cybersecurity for defending the BD system against cyber attacks. In some applications using wearable devices, physiological and location-tracking data also causes concerns related to workplace privacy implications. These shortcomings give rise to uncertainties about the practical benefits and effectiveness of applying BD in O&G activities. The objective of this paper is to perform a systematic review of BD analytics within the context of the O&G industry. This paper attempts to evaluate technical and nontechnical factors affecting the adoption of BD technologies. The study includes BD development platforms, network architecture, data privacy implications, cybersecurity, and the opportunities and challenges of adopting BD technologies in the O&G industry.INDEX TERMS Big data analytics, O&G digitalization, Industry 4.0, data privacy and security.

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Section: Review Findings and Discussionmentioning

confidence: 99%

Section: ) Apache Sparkmentioning

confidence: 99%

A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

2020

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“…The algorithms used in their model included Support Vector Machine (SVM), Random Forest, Naïve Bayes, XG Boost, and K-Means Clustering. Mohammadmoradi, et al [27] proposed a model to embed artificial intelligence algorithms in reservoir uncertainty modeling and present a mechanistically-supported data-driven model applicable for production forecasting of gas condensate wells with higher confidence. The outcome entails a new set of mathematical models, implemented using Apache Spark cluster computing engine with APIs in Python, that enables rigorous and robust optimization of the recovery process, designing and discovering hidden patterns in production data, and extracting reservoir information indirectly in seconds.…”

Section: Artificial Intelligencementioning

confidence: 99%

Review of Python Applications in Solving Oil and Gas Problems

Okon

Appah

Ajienka

2021

JERR

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Python has grown in popularity throughout various industries, corporations, universities, government, and research groups. Its true potential to automate various processes while increased predictability capabilities have been noticed in various industries. The petroleum industry is at the beginning phase of applying it to solve oil and gas problems. The rise in its popularity in the oil and gas industry is due to the digital transformation such as sensors and high-performance computing services that enable artificial intelligence (AI), machine learning (ML), big data acquisition, and storage in digital oilfields. A quick search on the number of publications in the oil and gas industry with the Society of Petroleum Engineers (OnePetro) in the past few years attests to this fact. Hence, it has proven to be a promising application that can bring about a revolutionary change in the oil and gas industry and transform the existing features for solving oil and gas problems. This will help the production and reservoir engineers to better manage the production operation without any need for expensive software. It will also reduce the overall operating cost and increase revenue.

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“…Their model used SVM, Random Forest, Naïve Bayes, XG Boost, and K-Means Clustering. Mohammadmoradi et al (2018) presented a mechanistically-supported data-driven model for gas condensate well production forecasting using artificial intelligence algorithms. A novel set of mathematical models implemented using Apache Spark cluster computing engine with Python APIs allows rigorous and resilient optimization of the recovery process, creating and identifying hidden patterns in production data and indirectly collecting reservoir information in seconds.…”

Section: Smart Energy Field With Pythonmentioning

confidence: 99%

A Commentary on the Applications of Python in Resolving Issues Concerning Energy and the Ecosystems

Fadziso¹

2021

Asia Pac. j. energy environ.

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The energy industry is just getting started with applying it to problems with energy and ecosystems so they can find solutions. Python's popularity has increased across a variety of sectors, including businesses, academic institutions, government agencies, and research organizations. The true potential it possesses to automate a variety of processes while simultaneously increasing the capabilities of various industries to predict outcomes has been observed. Because of the digital transformation, such as sensors and high-performance computing services, which enable artificial intelligence (AI), machine learning (ML), big data acquisition, and storage in digital oilfields, its popularity has been on the rise in the industry that deals with energy and ecosystems. This is one of the primary reasons why. This can be easily verified by conducting a quick search for the number of publications that have been produced in the field of energy and ecosystems by the Society of Petroleum Engineers over the past few years. Without having to invest in pricey software, the production and reservoir engineers will be able to better manage the production operation thanks to this development. In addition to this, it will lead to a decrease in the overall operating costs and an increase in revenue. As a result, it has been demonstrated to be a promising application that has the potential to bring about a revolutionary change in the industry of energy and ecosystems and to transform the features that are already in place for the purpose of resolving issues related to energy and ecosystems.

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Data-Driven Production Forecasting of Unconventional Wells with Apache Spark

Cited by 5 publications

References 6 publications

A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0

Review of Python Applications in Solving Oil and Gas Problems

A Commentary on the Applications of Python in Resolving Issues Concerning Energy and the Ecosystems

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