Geological Modeling Technology and Application Based on Seismic Interpretation Results under the Background of Artificial Intelligence

The development of seismic technology has made seismic data to be widely used in the interpretation of stratigraphic sequence frames, reservoir identification, fluid detection, and other research fields involved in reservoir description. The 3D technology reservoirs have always been the focus, as well as difficulty, of research. With the rapid development of information technology and the continuous improvement of seismic exploration level, people have put forward higher requirements for the accuracy of seismic data interpretation results. Aiming at the large number of structural and unstructured data in seismic, logging, geology, and other disciplines involved in seismic interpretation, how to effectively organize and coordinate analysis to discover the hidden reservoir structure and oil and gas distribution information has always been a geological and important topic for information processing technicians. This thesis is aimed at the current high-water-phase development of Shengtuo Oilfield reservoir and the problems existing in geological research. Based on seismic structural interpretation and attribute analysis, this paper analyzes the reservoir structural characteristics, sedimentary characteristics, and reservoir physical parameter characteristics based on geology, logging interpretation, core analysis, drilling, and seismic interpretation. Using the kriging method with external drift can cooperate with seismic variables to establish a reservoir geological model to study the Shengtuo Oilfield reservoir. We combine artificial intelligence technology with geological modeling technology of seismic interpretation results to explore the best method for predicting earthquakes. The research results in this paper show that the relative error of the model established by the kriging method in the article is relatively small for thinning wells, mainly concentrated around 1%. Examination of the thinning wells of 45 wells shows that the model established is basically good and the example has high accuracy. The research results in this paper have a guiding study of distribution and tapping potentials in the study area, formulating reasonable development and adjustment plans and improving oil recovery.

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Seismic Reservoir Characterisation of Tyumen Formation in Frolov Megadepression

10.2118/206592-ms ◽

2021 ◽

Author(s):

Anton Grinevskiy ◽

Irina Kazora ◽

Igor Kerusov ◽

Dmitriy Miroshnichenko

Keyword(s):

Seismic Data ◽

Middle Jurassic ◽

Oil And Gas ◽

Seismic Interpretation ◽

Hydrocarbon Potential ◽

3D Seismic ◽

Geological Modeling ◽

High Hydrocarbon ◽

Seismic Reservoir ◽

3D Seismic Data

Abstract The article discusses the approaches and methods to study the Middle Jurassic deposits of the Tyumen Formation within the Frolov megadepression (West Siberian oil and gas province), which have high hydrocarbon potential. The materials refer to several areas with available 3D seismic data and several dozen oil wells. The problems of seismic interpretation and its application for geological modeling are considered. We also propose several ways to overcome them.

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First Case Study for Litho-Petro-Elastic AVA Pre-Stack Inversion for Complex Tight Reservoirs Miocene – Upper Cretaceous in East Onshore Abu Dhabi

10.2118/208090-ms ◽

2021 ◽

Author(s):

Khalid Obaid ◽

Abdelwahab Noufal ◽

Abdulrahman Almessabi ◽

Atef Abdelaal ◽

Karim Elsadany ◽

...

Keyword(s):

Seismic Data ◽

Upper Cretaceous ◽

Reservoir Characterization ◽

Rock Physics ◽

Seismic Interpretation ◽

Abu Dhabi ◽

Seismic Exploration ◽

Frequency Model ◽

Structural Framework ◽

First Case

Abstract This study summarizes the efforts taken to provide reliable reservoir characterizations products to mitigate seismic interpretation challenges and delineation of the reservoirs. ADNOC has conducted seismic exploration activities to assess Miocene to Upper Cretaceous aged reservoirs in East Onshore Abu Dhabi. The Oligo-Miocene section comprises of interbedded salt (mainly halite), anhydrite, limestones and marls. Deposited in the foreland basin related to the Oman thrust-belt. Ranging in thickness from nearly 1.5 km in the depocenter to almost nil on the forebulge located to the west of the studied area. The well data based geological model suggests that initially porous rocks (presumably grain-supported carbonates) encompassed polyphase sulfate cementation during recurrent subaerial exposure in which pores and grains were recrystallized sometimes completely too massive, tight anhydrite beds. This heterogeneity of the complex shallow section showing high variation of velocity impact seismic imaging, and interpretation to model the stratigraphic/structural framework and link it with reservoir characterization. Hence, ADNOC decided to conduct a trial on state-of-art technique Litho-Petro-Elastic (LPE) AVA Inversion to mitigate the seismic interpretation challenges and delineate the reservoirs. The LPE AVA inversion provides a single-loop approach to reservoir characterization based on rock physics models and compaction trends, reducing the dependency on a detailed prior the low frequency model, Where the rock modelling and lithology classification are not separate steps but interact directly with the seismic AVO inversion for optimal estimates of lithologies and elastic properties. The LPE inversion scope requires seismic data conditioning such as CMP gathers de-noising, de-multiple, flattening and amplitude preservation, in addition to detailed log conditioning, petro-elastic and rock physics analysis to maximize the quality and value of the results. The study proved that the LPE AVA Inversion can be used to guide seismic interpreters in mapping the structural framework in challenging seismic data, as it managed to improve the prospect evaluation.

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Fault and horizon automatic interpretation by CNN: a case study of coalfield

Journal of Geophysics and Engineering ◽

10.1093/jge/gxaa060 ◽

2020 ◽

Vol 17 (6) ◽

pp. 1016-1025

Author(s):

Yinling Guo ◽

Suping Peng ◽

Wenfeng Du ◽

Dong Li

Keyword(s):

Seismic Data ◽

Seismic Interpretation ◽

Seismic Exploration ◽

Automatic Interpretation ◽

Manual Intervention ◽

History Of ◽

Model Training ◽

The Relationship ◽

3D Seismic Data

Abstract A convolutional neural network (CNN) is a powerful tool used for seismic interpretation. It does not require manual intervention and can automatically detect geological structures using the pattern features of the original seismic data. In this study, we presented the development history of seismic interpretation and the application of CNN in seismic exploration. We proposed a set of CNN prediction methods and processes for coalfield seismic interpretation and realised automatic interpretation of faults and horizons based on the relationship between faults and horizons. We defined a CNN model training method based on structural geological modelling, which allowed rapid and accurate establishment of fault and horizon labels by using structural modelling. We used two examples to verify the accuracy of the algorithm, one to test for synthetic 3D seismic data and one to test for real coalfield seismic data. The results showed that CNNs can effectively predict both faults and horizons at the same time and has high accuracy. Thus, CNNs are potentially novel interpretation tools for coalfield seismic interpretation.

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The Oxford Handbook of Ethics of AI

10.1093/oxfordhb/9780190067397.001.0001 ◽

2020 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Rapid Development ◽

Data Driven ◽

International Human Rights Law ◽

Human Knowledge ◽

Human Rights Law ◽

Ai Ethics ◽

And Behavior ◽

Control Complex

This book explores the intertwining domains of artificial intelligence (AI) and ethics—two highly divergent fields which at first seem to have nothing to do with one another. AI is a collection of computational methods for studying human knowledge, learning, and behavior, including by building agents able to know, learn, and behave. Ethics is a body of human knowledge—far from completely understood—that helps agents (humans today, but perhaps eventually robots and other AIs) decide how they and others should behave. Despite these differences, however, the rapid development in AI technology today has led to a growing number of ethical issues in a multitude of fields, ranging from disciplines as far-reaching as international human rights law to issues as intimate as personal identity and sexuality. In fact, the number and variety of topics in this volume illustrate the width, diversity of content, and at times exasperating vagueness of the boundaries of “AI Ethics” as a domain of inquiry. Within this discourse, the book points to the capacity of sociotechnical systems that utilize data-driven algorithms to classify, to make decisions, and to control complex systems. Given the wide-reaching and often intimate impact these AI systems have on daily human lives, this volume attempts to address the increasingly complicated relations between humanity and artificial intelligence. It considers not only how humanity must conduct themselves toward AI but also how AI must behave toward humanity.

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Application of Artificial Intelligence to Predict Enhanced Oil Recovery Using Silica Nanofluids

Natural Resources Research ◽

10.1007/s11053-021-09829-1 ◽

2021 ◽

Author(s):

Mahdi Shayan Nasr ◽

Hossein Shayan Nasr ◽

Milad Karimian ◽

Ehsan Esmaeilnezhad

Keyword(s):

Artificial Intelligence ◽

Enhanced Oil Recovery ◽

Oil Recovery

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Co@N-CNT/MXene in situ grown on carbon nanotube film for multifunctional sensors and flexible supercapacitor

Nanoscale ◽

10.1039/d1nr03641f ◽

2021 ◽

Author(s):

Qiufan Wang ◽

Jiaheng Liu ◽

Guofu Tian ◽

Daohong Zhang

Keyword(s):

Artificial Intelligence ◽

Carbon Nanotube ◽

Energy Storage ◽

Storage Systems ◽

Rapid Development ◽

Human Machine Interface ◽

Key Factors ◽

Flexible Supercapacitor ◽

Machine Interface

The rapid development of human-machine interface and artificial intelligence is dependent on flexible and wearable soft devices such as sensors and energy storage systems. One of the key factors for...

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Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

Journal of Petroleum Technology ◽

10.2118/0121-0012-jpt ◽

2021 ◽

Vol 73 (01) ◽

pp. 12-13

Author(s):

Manas Pathak ◽

Tonya Cosby ◽

Robert K. Perrons

Keyword(s):

Artificial Intelligence ◽

Science Fiction ◽

Oil Recovery ◽

Oil And Gas ◽

Autonomous Systems ◽

Oil And Gas Industry ◽

Human Judgment ◽

Gas Industry ◽

The Media ◽

And Function

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

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Using AI/ML to Explore & Develop Quickly and Efficiently

10.2118/207377-ms ◽

2021 ◽

Author(s):

Anthony Aming

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Seismic Data ◽

3D Seismic ◽

Structure Amplitude ◽

Zero Crossing ◽

Petroleum Systems ◽

Exploration And Production ◽

Data Volume ◽

3D Seismic Data

Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

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DEEPFAKE AS A PHENOMENON OF POLITICAL COMMUNICATION

Transbaikal State University Journal ◽

10.21209/2227-9245-2021-27-6-101-106 ◽

2021 ◽

Vol 27 (6) ◽

pp. 101-106

Author(s):

М. Falaleev ◽

◽

N. Sitdikova ◽

Е. Nechay ◽

◽

...

Keyword(s):

Artificial Intelligence ◽

Political Communication ◽

Political Discourse ◽

Rapid Development ◽

Theoretical Data ◽

Practical Significance ◽

Subject Field ◽

Learning Programs ◽

Definition Of ◽

The Impact

The development of digital technologies, coupled with progress in the development of self-learning programs based on AI (Artificial Intelligence), has obvious advantages in improving the effectiveness of information impact on people around the world. During the 2010s, researchers have documented trends in the use of artificial intelligence for the construction and distribution of media content to indirectly manipulate political discourse at the national and global levels. Special interest in the context of this issue is how the rapid development of AI technologies affects political communication. The object of consideration within the framework of this article is the deepfake technology. Based on this, as a subject, the authors define deepfake as a phenomenon of modern political communication. Accordingly, the purpose of the study is to describe and predict the impact of deepfake technology on political communication at the global and national levels. The paper presents the definition of deepfake, assesses its characteristics depending on the methods and purposes of its distribution, and analyzes the prospects for using this tool to influence political discourse in modern Russia. To study the subject field of the research, methods of systematizing theoretical data, classification, analysis of a set of factors and forecasting have been applied. The practical significance of the work is presented by the authors’ definition and typology of the phenomenon of deepfake and describes its significance as a factor of political communication on the example of a particular country. The results of the work will be useful for researchers studying the problems of digitalization of the media space and modern means of disinformation in politics, both at the local and global levels

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Combined Application of Deep Boundary Detection Tool, Multilayer Data Inversion and 3D Visualization of Seismic Data in Real Time for Geosteering on the Oilfield in the Russian Federation

10.2118/207035-ms ◽

2021 ◽

Author(s):

Chingis Oshakbayev ◽

Roman Romanov ◽

Valentin Vlassenko ◽

Simon Austin ◽

Sergey Kovalev ◽

...

Keyword(s):

Real Time ◽

Oil Recovery ◽

Seismic Data ◽

3D Visualization ◽

Structural Data ◽

Boundary Detection ◽

Integrated Analysis ◽

Horizontal Section ◽

Recovery Method ◽

Detection Tool

Abstract Currently drilling of horizontal wells is a common enhanced oil recovery method. Geosteering services are often used for accurate well placement, which makes it possible to achieve a significant increase in production at relatively low cost. This paper describes the result of using seismic data in three-dimensional visualization for high-quality geosteering using a deep boundary detection tool and multilayer inversion in real time. Crossing the top of the reservoir while drilling horizontal sections at the current oilfield is unacceptable, due to the presence of reactive mudstones. In case of crossing the top of reservoir, further work on running and installing the liner becomes impossible due to instability and may lead to well collapse. Based on prewell analysis of the structural data, the well was not supposed to approach the top of the target formation along the planned profile. However, while preparing geosteering model and analyzing seismic data it became possible to reveal that risk, elaborate its mitigation and eventually increase the length of the horizontal section. Such integrated analysis made it possible to maintain the wellbore within the target reservoirs, as well as to update the structural bedding of the top based on the multilayer inversion results.

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