Africa (Sub-Sahara) Drilling began on the Bamboo-1 well, located around 35 miles offshore Cameroon in the Ntem concession. The Bamboo prospect is a basin floor fan target within an Upper Cretaceous play. The well will be drilled to an estimated depth of 4200 m. Murphy Cameroon (50%) is the operator, with partner Sterling (50%). The Nene Marine 3 exploration well--located in the Marine XII block, which is around 17 km offshore Congo--encountered a wet gas and light oil accumulation in a presalt clastic sequence Eni (65%) operates the Marine XII block, with partners New Age (25%) and Société Nationale des Pétroles du Congo (10%). CNPC said PetroChina is now building a production facility capable of pumping 4 Bcm/yr.
Below is a list of basins and fields; however this is a short list since there are more than 65,000 oil and gas basins and fields of all sizes in the world. However, 94% of known oil fields is concentrated in fewer than 1500 giant and major fields. Most of the world's largest oilfields are located in the Middle East, but there are also supergiant ( 10 billion bbls) oilfields in India, Brazil, Mexico, Venezuela, Kazakhstan, and Russia. Add any basins or fields that are missing from this list!
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).
Seismic attributes are a well-established method for highlighting subtle features buried in seismic data in order to improve interpretability and suitability for quantitative analysis. Seismic attributes are a critical enabling technology in such areas thin bed analysis, 3D geobody extraction, and seismic geomorphology. When it comes to seismic attributes, we often suffer from an "abundance of riches" as the high dimensionality of seismic attributes may cause great difficulty in accomplishing even simple tasks. Spectral decomposition, for instance, typically produces 10's and sometimes 100's of attributes. However, when it comes to visualization, for instance, we are limited to visualizing three or at most four attributes simultaneously.
My co-authors and I first proposed the use of latent space analysis to reduce the dimensionality of seismic attributes in 2009. At the time, we focused upon the use of non-linear methods such as self-organizing maps (SOM) and generative topological maps (GTM). Since then, many other researchers have significantly expanded the list of unsupervised methods as well as supervised learning. Additionally, latent space methods have been adopted in a number of commercial interpretation and visualization software packages.
In this paper, we introduce a novel deep learning-based approach to latent space analysis. This method is superior in that it is able to remove redundant information and focus upon capturing essential information rather than just focusing upon probability density functions or clusters in a high dimensional space. Furthermore, our method provides a quantitative way to assess the fit of the latent space to the original data.
We apply our method to a seismic data set from the Canterbury Basin, New Zealand. We examine the goodness of fit of our model by comparing the input data to what can be reproduced from the reduced dimensional data. We provide an interpretation based upon our method.
Agrawal, Dhruv (The University of Texas of the Permian Basin) | Lujan, Brady (The University of Texas of the Permian Basin) | Verma, Sumit (The University of Texas of the Permian Basin) | Mallick, Subhashis (The University of Wyoming)
The Green River Basin in the SW Wyoming is responsible for all production within Lincoln, Sublette, Sweetwater, and Uinta Counties in Wyoming. This study focuses on peculiar features in the Lincoln County, we call them FLTs (funny looking things), observed in the seismic data associated with the Triassic/Jurassic deposition in the Moxa Arch. The acquisition and processing errors cannot explain these features, which led us to look for a geologic explanation. Well to seismic ties on three wells surrounding the seismic survey indicated that the observed FLTs on seismic correspond to the Jurassic aged Nugget Sandstone formation. Based on the seismic inversion and the petrophysical model, we concluded that the lithology distribution is comprised of dunal and inter-dunal deposits.
Presentation Date: Wednesday, October 17, 2018
Start Time: 1:50:00 PM
Location: Poster Station 12
Presentation Type: Poster
Many companies are now refocusing on play-based exploration, representing a return to our pre-1990s geoscience roots. Play-based exploration involves understanding all elements of the petroleum system for a given basin or play and then examining how, and more importantly where, those elements come together. This paper will examine the fundamental methods using examples across Asia and review step by step procedures of the play-based exploration process.
Such 3D surveys are often accompanied by previously acquired 2D regional lines. Yet, due to the 2D nature and older acquisition technique, these 2D lines are usually of lower quality and contain more noise than the associated 3D data. In this project, we improve seismic image quality, identify regional features and local anomalies, and analyze seismic facies that are potentially related to hydrocarbon production in the Exmouth Plateau, North Carnarvon Basin, Australia, by simultaneously applying data conditioning, seismic attribute calculation, and Self-Organizing-Map (SOM) classification to multiple vintage 2D lines. Introduction The North Carnarvon Basin is a major hydrocarbon reserve in Australia (Chongzhi et al, 2013). It can be divided into sub-basins (Figure 1). Among these sub-basins, the Exmouth Plateau is the largest and cover most of the major gas fields. Thanks to such a prolific amount of hydrocarbon reserve, numerous seismic surveys have been acquired over the area.
Pattern recognition based multiattribute seismic facies analysis enables seismic interpreters to effectively extract and analyze information buried in several seismic attributes. However, most pattern recognition methods rely heavily on training data, which means the algorithms detect features that best represent the training data. In this study, using self-organizing map as an example of pattern recognition techniques, we discuss the influence (and sometimes, bias) associated with different training data selection strategies. We further demonstrate that using the same attributes, different training samples may lead to different interpretation of seismic facies.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Presentation Type: ORAL
Unsupervised seismic facies are a convenient and efficient tool for interpretation. Expanding upon Zhao et al.'s (2016) study, Gaussian mixture models are used to show how features can automatically be generated using machine learning. The conventional expectation-maximization algorithm is compared to the neighborhood expectation-maximization algorithm to highlight the effects of spatial relations in the data in addition to the measurements of seismic attributes. The survey being used is a 3D seismic survey from the Canterbury basin, New Zealand called Waka-3D
Presentation Date: Tuesday, September 26, 2017
Start Time: 10:35 AM
Location: Exhibit Hall C, E-P Station 1
Presentation Type: EPOSTER
The hyperbolic Radon transform is a commonly used tool in seismic processing, for instance in seismic velocity analysis, data interpolation and for multiple removal. A direct implementation by summation of traces with different moveouts is computationally expensive for large data sets. In this paper we present a new method for fast computation of the hyperbolic Radon transforms. It is based on using a log-polar sampling with which the main computational parts reduces to computing convolutions. This allows for fast implementations by means of FFT. In addition to the FFT operations, interpolation procedures are required for switching between coordinates in the time-offset; Radon; and log-polar domains. Graphical Processor Units (GPUs) are suitable to use as a computational platform for this purpose, due to the hardware supported interpolation routines as well as optimized routines for FFT. Performance tests show large speed-ups of the proposed algorithm. Hence, it is suitable to use in iterative methods for retrieving sparse (high-resolution) Radon representations. Examples are presented for this, as well as examples for data interpolation and multiple removal.
Presentation Date: Tuesday, October 18, 2016
Start Time: 8:50:00 AM
Presentation Type: ORAL