Seismic interpretation in the age of big data

Roden, Rocky

doi:10.1190/segam2016-13612308.1

Cited by 15 publications

(16 citation statements)

References 2 publications

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“…Scaled Poisson s Ratio = aA + bB (5) From the Aki-Richards Equation, scaled S-wave reflectivity can also be derived by assuming that Vp/Vs = 2 at intermediate angles (0 • < θ < 30 • ). The Equation ( 6) is expressed as follows:…”

Section: R(θmentioning

confidence: 99%

“…Unsupervised learning approaches are being used in geoscience to assist AVO cross-plotting in characterizing its background trend [3]. Another example is that unsupervised learning can make recommendations about which attributes to use in multi-attribute analysis [4,5]. Unsupervised learning can also be utilized to detect the geological depositional environment [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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Amplitude versus offset (AVO) analysis and attributes are frequently utilized during the early stages of exploration when no well has been drilled. However, there are still some drawbacks to this method, including the fact that it involves a substantial amount of time and experience, as well as the subjectivity of manual analysis. By utilizing unsupervised learning, this process can be done more objectively and faster. Unsupervised learning can detect anomalies and identify patterns to understand more about the datasets since, at this early stage of exploration, there is still a lack of information and labelling. A type of unsupervised learning referred to as self-organizing maps (SOM) is applied in this study to delineate hydrocarbons from given AVO properties that were used to detect hydrocarbons. SOM is also used to eliminate redundancy in the selection of attributes prior to the delineation procedure. The investigation began with well log data and progressed ahead into multiple fluid conditions to evaluate the model’s ability to identify hydrocarbons. The analysis can then be extended to the seismic dataset. By combining SOM, correlation coefficient, and mean–median, a method is devised for filtering features to remove redundancy. On the hydrocarbon delineation process, the model managed to detect hydrocarbons using well log simulations and was confirmed using water saturation logs. Additionally, the model is validated using real seismic data, demonstrating a promising performance in defining probable hydrocarbons. The proposed method enables early detection of hydrocarbon content during the preliminary stage of exploration when no well is accessible.

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Section: R(θmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pay Zone Determination Using Enhanced Workflow and Neural Network

Syahputra

Hermana

Satti³

2022

Applied Sciences

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“…Big data analytics has been using a range of tools and techniques, including use of 3D plane to show the relationship between data, science, technology, engineering, and mathematics (STEM) tools, and pattern recognition [100]- [102]. Machine learning tools can reveal the relationship between the recorded data more efficiently, specifically for the recent trend dealing with huge datasets [103]. A Hadoop platform, instead of conventional tools, can manage the massive datasets generated by micro-seismic tools, improving the success ratio by detecting the potential anomalies from previous failed jobs [104].…”

Section: Decision Support Through Big Data Analyticsmentioning

confidence: 99%

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

et al. 2021

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Working at an oil and gas facility, such as a drilling rig, production facility, processing facility, or storage facility, involves various challenges, including health and safety risks. It is possible to leverage emerging digital technologies such as smart sensors, wearable or mobile devices, big data analytics, cloud computing, extended reality technologies, robotic systems, and drones to mitigate the challenges faced by oil and gas workers. While these technologies are not new to the oil and gas industry, most of its existing digital transformation initiatives follow business or process-centric approaches, in which the critical driver of the technology adoption is the enhancement of production, efficiency, and revenue. As a result, they may not address the challenges faced by the workers. As oil and gas workers are among the essential assets in the oil and gas industry, it is vital to address the challenges faced by these workers. This paper proposes a human-centric digital transformational framework for the oil and gas industry to deploy existing digital technologies to enhance their workers' health, safety, and working conditions. The paper outlines the critical challenges faced by oilfield workers, introduces a system architecture to implements a human-centric digital transformation, discusses the opportunities of the proposed framework, and summarizes the key impediment for the proposed framework.

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“…Karya tulis ini bertujuan untuk melakukan interpretasi geologi terhadap penampang seismik tersebut. Interpretasi penampang seismik bertujuan untuk menampilkan gambaran informasi geologi dari hasil pengolahan data seismik dengan dukungan atribut-atribut seismik (Roden, 2016 Seismik metode multi channel yaitu sistem perekaman data lapangan terdiri dari satu sumber seismik dan banyak penerima, gelombang-gelombang seismik refleksi tiba ke permukaan hampir bersamaan sehingga hasil yang didapatkan lebih jelas dibandingkan metode single channel. Pada metode multi channel ini karena terdiri dari banyak penerima maka akan menghasilkan demutiple atau perulangan lapisan yang dapat menggangu interpretasi sehingga harus dihilangkan (Ikeda dan Tsuji, 2016).…”

Section: Pendahuluanunclassified

Interpretasi Struktur Geologi Di Perairan Aru Selatan, Maluku Berdasarkan Data Seismik 2D Multi Channel

Ustiawan

Nainggolan

Setyawan

2019

Jurnal Geosains dan Teknologi

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PProduksi minyak di Indonesia telah dimulai sejak tahun 1885 ketika sumur minyak pertama di Indonesia berhasil berproduksi pada Kabupaten Langkat, Sumatera Utara. Faktor pemulihan yang rendah mengakibatkan produksi minyak di Indonesia telah menurun karena kurangnya eksplorasi dan investasi pada sektor minyak bumi. Salah satu metode geofisika yang digunakan dalam eksplorasi hidrokarbon yang pada umumnya memiliki kedalaman yang dalam yaitu adalah metode seismik. Penelitian ini bertujuan untuk melakukan interpretasi geologi terhadap penampang seismik sehingga menampilkan gambaran bawah permukaan. Pusat Penelitian dan Pengembangan Geologi Kelautan (PPPGL) merupakan unit penelitian dan pengembangan dibawah Kementerian Energi dan Sumber Daya Mineral yang melakukan tahapan pengambilan data seismik refleksi terutama di daerah laut. Tulisan ini menggunakan data seismik refeksi pada Line-14, Laut Aru Selatan, Maluku. Data metode seismik multi channel sebanyak 1 lintasan tersebut kemudian diproses menggunakan perangkat lunak pengolahan data seismik. Proses interpretasi menggunakan perangkat lunak interpretasi geologi dengan melakukan picking horizon dari batas formasi dan struktur geologi. Berdasarkan tahapan interpretasi, tatanan stratigrafi daerah penelitian memiliki kesebandingan atas Formasi Kemum, Formasi Kais, Formasi Klasafet, dan Formasi Klasaman bagian bawah. Berdasarkan interpretasi struktur geologi didapatkan kumpulan sesar normal yang dapat diindikasikan sebagai migration pathway dari hidrokarbon.

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Seismic interpretation in the age of big data

Cited by 15 publications

References 2 publications

Pay Zone Determination Using Enhanced Workflow and Neural Network

Pay Zone Determination Using Enhanced Workflow and Neural Network

Human Centric Digital Transformation and Operator 4.0 for the Oil and Gas Industry

Interpretasi Struktur Geologi Di Perairan Aru Selatan, Maluku Berdasarkan Data Seismik 2D Multi Channel

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