Geophysical data registration using modified plane-wave destruction filters

I propose a method to efficiently measure local shifts, slopes, and scaling functions between seismic traces using modified plane-wave destruction filters. Plane-wave destruction can efficiently measure shifts of less than a few samples, making this algorithm particularly effective for detecting small shifts. When shifts are large, amplitude-adjusted plane-wave destruction can also be used to refine shift estimates obtained by other methods. Amplitude-adjusted plane-wave destruction separates estimation of local shifts and amplitude weights, allowing the time-shift to be measured more accurately. This algorithm has clear applications to geophysical data registration problems, including time-lapse image registration, multicomponent image registration, automatic gather flattening, automatic seismic-well ties, and image merging. The effectiveness of this algorithm in predicting shifts associated with fluid migration, wave mode conversions, and anisotropy and amplitude gradients associated with amplitude variations with offset or angle is demonstrated by applying the algorithm to a synthetic trace, a time-lapse field data example from the Cranfield CO₂ sequestration project, a multicomponent field data example from West Texas, and the Mobil AVO prestack seismic data. Finding correspondence between different parts of the same dataset falls into the same category of problems as local shift estimation. Computation of structure-oriented amplitude gradients for attribute-assisted interpretation requires the estimation of local slopes by correlating reflections between neighboring seismic traces in an image. One of the major challenges of interpreting seismic images is the delineation of reflection discontinuities that are related to geologic features, such as faults, channels, salt boundaries, and unconformities. Visually prominent reflection features often overshadow these subtle discontinuous features which are critical to understanding the structural and depositional environment of the subsurface. For this reason, precise manual interpretation of these reflection discontinuities in seismic images can be tedious and time-consuming, especially when data quality is poor. Discontinuity enhancement attributes are commonly used to facilitate the interpretation process by enhancing edges in seismic images and providing a quantitative measure of the significance of discontinuous features. These attributes require careful pre-processing to maintain geologic features and suppress acquisition and processing artifacts which may be artificially detected as a geologic edge. The plane-wave Sobel filter cascades plane-wave destruction filters with plane-wave shaping in the transverse direction to compute an enhanced discontinuity attribute. The plane-wave Sobel attribute can be applied directly to a seismic image to efficiently and effectively enhance discontinuous features, or to a coherence image to create a sharper and more detailed image. I demonstrate the effectiveness of this method by applying it to two field data sets from offshore New Zealand and offshore Nova Scotia with several faults and channel features and compare the results to other coherence attributes.Geological Science

Ontology based data warehouse modelling - a methodology for managing petroleum field ecosystems

Petroleum field ecosystems offer an interesting and productive domain for ontology based data warehousing model and methodology development. This paper explains the opportunities and challenges confronting modellers, methodologists, and managers operating in the petroleum business and provides some detailed techniques and suggested methods for constructing and using the ontology based warehouse.Ecologically sensitive operations such as well drilling, well production, exploration, and reservoir development can be guided and carefully planned based on data mined from a suitable constructed data warehouse. Derivation of business intelligence, simulations and vizualisation can also be driven by online analytical processing based on warehoused data and metadata

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management

The North West Shelf (NWS) interpreted as a Total Petroleum System (TPS), is Super Westralian Basin with active onshore and offshore basins through which shelf, - slope and deep-oceanic geological events are construed. In addition to their data associativity, TPS emerges with geographic connectivity through phenomena of digital petroleum ecosystem. The super basin has a multitude of sub-basins, each basin is associated with several petroleum systems and each system comprised of multiple oil and gas fields with either known or unknown areal extents. Such hierarchical ontologies make connections between attribute relationships of diverse petroleum systems. Besides, NWS has a scope of storing volumes of instances in a data-warehousing environment to analyse and motivate to create new business opportunities. Furthermore, the big exploration data, characterized as heterogeneous and multidimensional, can complicate the data integration process, precluding interpretation of data views, drawn from TPS metadata in new knowledge domains. The research objective is to develop an integrated framework that can unify the exploration and other interrelated multidisciplinary data into a holistic TPS metadata for visualization and valued interpretation. Petroleum digital ecosystem is prototyped as a digital oil field solution, with multitude of big data tools. Big data associated with elements and processes of petroleum systems are examined using prototype solutions. With conceptual framework of Digital Petroleum Ecosystems and Technologies (DPEST), we manage the interconnectivity between diverse petroleum systems and their linked basins. The ontology-based data warehousing and mining articulations ascertain the collaboration through data artefacts, the coexistence between different petroleum systems and their linked oil and gas fields that benefit the explorers. The connectivity between systems further facilitates us with presentable exploration data views, improvising visualization and interpretation. The metadata with meta-knowledge in diverse knowledge domains of digital petroleum ecosystems ensures the quality of untapped reservoirs and their associativity between Westralian basins

Reservoir characterization using intelligent seismic inversion

Integrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir\u27s facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field

Machine assisted quantitative seismic interpretation

During the past decades, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. Reducing the labor associated with seismic interpretation while increasing the reliability of the interpreted result has been an on going challenge that becomes increasingly more difficult with the amount of data available to interpreters. To address this issue, geoscientists often adopt concepts and algorithms from fields such as image processing, signal processing, and statistics, with much of the focus on auto-picking and automatic seismic facies analysis. I focus my research on adapting and improving machine learning and pattern recognition methods for automatic seismic facies analysis. Being an emerging and rapid developing topic, there is an endless list of machine learning and pattern recognition techniques available to scientific researchers. More often, the obstacle that prevents geoscientists from using such techniques is the “black box” nature of such techniques. Interpreters may not know the assumptions and limitations of a given technique, resulting in subsequent choices that may be suboptimum. In this dissertation, I provide a review of the more commonly used seismic facies analysis algorithms. My goal is to assist seismic interpreters in choosing the best method for a specific problem. Moreover, because all these methods are just generic mathematic tools that solve highly abstract, analytical problems, we have to tailor them to fit seismic interpretation problems. Self-organizing map (SOM) is a popular unsupervised learning technique that interpreters use to explore seismic facies using multiple seismic attributes as input. It projects the high dimensional seismic attribute data onto a lower dimensional (usually 2D) space in which interpreters are able to identify clusters of seismic facies. In this dissertation, using SOM as an example, I provide three improvements on the traditional algorithm, in order to present the information residing in the seismic attributes more adequately, and therefore reducing the uncertainly in the generated seismic facies map

Application of seismic attributes and unsupervised machine learning methods for identification of hidden faults in basement and carbonate rocks

Seismic fault interpretation is a critical task for any type of energy industry and correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes such as coherence and curvature are routinely employed to enhance fault visualization in seismic data, but they can show limitations for sub-seismic faulting. Two projects are presented here showing how recently introduced geometric seismic attributes, such as total aberrancy, and unsupervised machine learning methods, such as self-organizing maps (SOM) and generative topographic mapping (GTM), can be applied for enhancing fault visualization. The first project focuses on an area with potential for CO2 storage in the carbonates of the Duperow Formation, northern Montana. In this study, we compared broadband and multispectral coherence, curvature, and aberrancy, and we compared SOM and GTM techniques when including and excluding aberrancy attributes. Our results showed that integrating aberrancy attributes during the multiattribute analysis and the machine learning steps considerably enhance the visualization of lineaments with strikes similar to those of fracture sets seen only with well log data and missed by the conventional geometric seismic attributes and the ML scenarios excluding aberrancy attributes. The second project is related to wastewater injection and induced seismicity in basement-rooted faults in northcentral Oklahoma. Here, different geometric seismic attributes were analyzed and integrated using unsupervised machine learning to identify potential basement-rooted faults and strike-slip-related structures. The machine learning results not only confirmed the existence of NE-SW faults that extend from the basement upward into the sedimentary section and that correlated with earthquake data but also the potential existence of other NE-SW structurally controlled features of anticlinorium shape