Seismic-based characterization of a carbonate gas storage reservoir assisted by machine learning techniques

Silurian pinnacle reefs found within the Michigan Basin were prolific hydrocarbon producers in the mid-to-late twentieth century. During production, studies over these complex reservoirs were primarily focused on facies distributions and depositional environments interpreted from core and petrophysical log data. 2-D seismic was applied primarily for reef identification, and rarely incorporated in identifying facies. To date, only two studies using modern 3-D seismic data to characterize Silurian pinnacle reefs have been published (Toelle and Ganshin, 2018; Buist 2020). Toelle and Ganshin (2018) had poor well control, which significantly reduced the certainty of interpretations made. Buist (2020) utilized unsupervised Self-Organizing Maps for porosity and permeability correlation from seismic data in several reefs along the Southern Reef Trend. This study is the first to conduct a pre-stack seismic inversion over a Silurian pinnacle reef within the Michigan Basin, and both the pre-stack inversion volumes and post-stack seismic attributes are integrated with supervised and unsupervised machine learning techniques (Probability Neural Networks and Generative Topographic Maps) to characterize the reservoir properties of Ray Reef field along the Southern Reef Trend. The workflow for this study begins with the well log data. A feasibility study is conducted to analyze relationships between elastic properties such as velocities and impedances, and reservoir properties such as porosity and lithology. Simultaneous pre-stack inversion is then conducted to provide P and S-impedance volumes, velocity cubes, and lambda-rho mu-rho volumes. Attributes are generated on the post-stack data as well, and input into the Generative Topographic Map (GTM) algorithm. The GTM is able to identify non-linear relationships between the attributes, and identifies relationships between lithology and seismic attributes. Pre-stack inversion attributes are analyzed in traditional crossplots to classify zones of good, fair, and poor porosity. Probability Neural Networks (PNNs) are shown to excel at classifying the gas-water contact within the reservoir, in addition to delineating salt units from the encasing carbonate units. The workflow described in this study identified a consistent relationship between lambda-rho and mu-rho attributes for porosity and possible fluid content within the Ray Reef gas storage reservoir in southeastern Michigan. Unsupervised machine learning techniques used also showed the ability to identify the reef core lithofacies from post-stack seismic data. These workflows have the potential to be applied on other pinnacle reef complexes within the Michigan Basin in addition to other carbonate reservoirs around the world

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

Seismic data conditioning, attribute analysis, and machine-learning facies classification: applications to Texas panhandle, Australia, New Zealand, and Gulf of Mexico

Whether analyzed by a human interpreter or by a machine learning algorithm, 3D seismic interpretation is only as good as the data that goes into it. The goal of seismic processing is to minimize noise and enhance signal to provide the most accurate image of the subsurface. Once imaged, the resulting migrated data volume can be further enhanced to suppress random and cross-cutting coherent noise and to better balance the spectrum to improve vertical resolution. Next, seismic attributes enhance subtle geologic features that may be otherwise overlooked. At this point, skilled human interpreters are very adept at not only seeing patterns in the data, but also in constructing correlations in their brain between multiple attributes and geologic features of interest. Machine learning algorithms are not yet at this point. Several machine learning algorithms require, and many perform better on data that exhibit Gaussian statistics, such that we need to carefully scale the attribute volumes to be analyzed. The application of filters that block and smooth the attribute volume, mimicking what a human interpreter “sees” provide further improvements. In this dissertation, I address most of these data conditioning challenges, as well as adapting and recoding the machine learning algorithms themselves. Conventional imaging of the shallow targets often results in severe migration aliasing. To improve the interpretation of a shallow fractured-basement reservoir in the Texas Panhandle, I developed a data conditioning technique called constrained conjugate-gradient least-squares migration to the prestack unmigrated data of the study area. I found that constrained conjugate-gradient least-squares migration can increase the signal-to-noise ratio, suppress migration artifacts, and improve seismic inversion results. Although 3D seismic surveys are routinely acquired, in frontier areas, much of our data consist of a grid of 2D seismic lines. Few publications discuss the application and limitations of modern seismic attributes to 2D lines, and fewer still the application of machine learning. I used a grid of 2D lines acquired over a turbidite channel system and carbonate sequences in the Exmouth Plateau, North Carnarvon Basin, Australia, to address this question. First, I modified 3D data conditioning workflows including nonlinear spectral balancing and structure-oriented filtering, and found that spectral balancing followed by structure-oriented filtering provides superior results. All of the more common attributes perform well, but with analysis of 2D lines providing apparent dip and apparent curvature in the inline direction rather than true dip magnitude and azimuth, and most-positive and most-negative curvature and their strikes. I analyzed coherence, curvature, reflector convergence, and envelope attributes using self-organizing maps and was able to successfully map turbidite canyon, carbonate mounds, and mass-transport complexes (MTCs) in the study area. Although some attributes exhibit Gaussian statistics, most do not. Although many machine learning algorithms are based on Gaussian statistics, most applications apply a simple Z-score normalization. I therefore compared the results of seismic facies classification of a Canterbury Basin turbidite system when using the traditional Z-score normalization versus one I developed that addresses skewness, kurtosis, and other scaling features in the attribute histogram. I found that logarithmic normalizations of skewed distributions are better input to unsupervised PCA, ICA, SOM, and GTM classification algorithms, but are worse for the supervised learning PNN classification algorithm. In contrast, supervised classification benefits greatly from a class-dependent normalization scheme, where the training data are normalized differently for each class

10th EASN International Conference on Innovation in Aviation & Space to the Satisfaction of the European Citizens

This Special Issue book contains selected papers from works presented at the 10th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held from the 2nd until the 4th of September, 2020. About 350 remote participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Eleven interesting articles, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue

Social, Economic, and Technical Feasibility of Ablution Gray Water Reuse in Tong, Ghana

Water insecurity is a constant stressor for millions of people around the globe. The potential of gray water as an alternative water source has gained increasing attention in the literature. In water-scarce Muslim communities, the practice of ablution – the rinsing of the face, hands, and feet before prayer – offers a unique opportunity for gray water reuse. This report investigates the feasibility of treating and reusing ablution gray water (AGW) with respect to religious acceptability, economic measures, and physical parameters in Tong, a rural community of Muslim subsistence farmers in northern Ghana. Investigatory tools included: household water use surveys, opinion leader interviews, wastewater collection prototype design, treatments identification and testing, and comparison to existing water soruces. A Ghanaian-made clay pot filter, coagulation and settling using moringa tree seeds, and P&G™ Purifier of Water were the treatments tested. Results were analyzed and compiled into a holistic, visual assessment tool termed a “Decision-making for Reuse of Ablution Wastewater (DRAW) Chart.” DRAW charts are an improved performance measure as they compare water sources based not simply on binary quality and time standards but on a spectrum that includes collection time, monetary investment, social acceptability, and water quality. The DRAW charts developed for Tong, including both current dry season water sources and gray water treatment options, indicate AGW reuse could be socially acceptable, has potential to provide quality water, and would be financially competitive with existing sources. AGW quantities cannot completely alleviate water insecurity but can offer a relatively quick and affordable supplementary water source without external assistance or the risk of failure often associated with other water solutions such as borehole drilling. The process used in this study and resultant DRAW charts can clarify the multifaceted complexities of water supply in water-insecure, financially disadvantaged communities beyond Tong. The development of DRAW charts for such communities provides community leaders and aid partners a clearer view of all aspects relevant to making water solution decisions and allowing more targeted and appropriate proposals under local budgetary constraints and norms

Machine Learning for Seismic Exploration: where are we and how far are we from the Holy Grail?

Machine Learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented to almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency and in some cases for improving the results. We carried out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derived a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extracted various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata shows that the main targets of ML applications for seismic processing are denoising, velocity model building and first break picking, whereas for seismic interpretation, they are fault detection, lithofacies classification and geo-body identification. Through the metadata available in publications, we obtained indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc. and we used them to approximate the level of efficiency, effectivity and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks show that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based QC is more effective and applicable compared to other processing tasks. Among the interpretation tasks, ML-based impedance inversion shows high efficiency, whereas high effectivity is depicted for fault detection. ML-based Lithofacies classification, stratigraphic sequence identification and petro/rock properties inversion exhibit high applicability among other interpretation tasks

Revealing channelized features through multi-scale workflows in a mixed carbonate siliciclastic setting, Grayburg and San Andres formations, Midland Basin, TX

Channelized systems in mixed carbonate-siliciclastic settings are challenging to characterize from the geological standpoint as facies variability is expected to be high (e.g., siliciclastic porous channel fills, carbonate cemented channel fills or even carbonate channel fills). Determining the lithological composition is crucial for not only understanding the basin evolution but also is required for drilling plans either if the channels serve as reservoirs or drilling hazards. An example of one such compositionally mixed channel system is identified in the San Andres and Grayburg formations in the Midland Basin, TX. For this specific example, channels are presumably siliciclastic infilled while the shelf the channels cut across is dominantly carbonate. An integrated study of core, well-log, and seismic data is conducted to analyze the facies variability of the channelized interval and understand its geomorphological evolution. Seismic attributes such as coherent energy, sweetness and spectral components (CWT) prove to be the most efficient at enhancing the contrast between the clastic vs carbonate elements; demonstrating that it is feasible to depict the lithological heterogeneity between the channel infills and the shelf at a seismic scale. Additionally, conventional seismic interpretation and geometric attributes (e.g., apparent dip, dip azimuth and magnitude, etc.) suggests two categories of channel incisions: type I, characterized by V-shaped bases, straight and mostly oriented in a NE-SW direction; and a type II, that tend to be U-shaped, slightly sinuous, and oriented in a NW-SE trend. Well-log based litho-density techniques such as ρmaa-Umaa and core descriptions support the seismic observations by illustrating the vertical and horizontal heterogeneity and how the channel infills are dominantly siliciclastic in nature. A 3D lithology model constrained to the previous analyses illustrates a dominance of siliciclastics in the Lower San Andres while the Upper San Andres and Grayburg are limestone-rich with episodic siliciclastic events (i.e., related to the channel incisions) and dolostone (in the Upper Grayburg). Lithologies and morphological changes are directly related to changes in the sea level and source rock composition. This study is pioneering in its understanding of the siliciclastic deposition in the middle Guadalupian units in this portion of the Midland Basin, which are referred in literature as the Midland sands and identified as analogs of the Brushy and Cherry Canyon formations in the Delaware Basin

RESERVOIR CHARACTERIZATION AND MODELING OF A CRETACEOUS TRIPLE POROSITY CARBONATE RESERVOIR CONTRIBUTION OF PORE TYPES TO HYDROCARBON PORE VOLUME AND PRODUCTION, CAMPECHE SOUND, GULF OF MEXICO

Campeche Sound, located southeast of the continental shelf in the Gulf of Mexico. represents about 80% of the national hydrocarbons production of Mexico, and comprises several giant oilfields, including Cantarell and Ku-Zaap-Maloob. The reservoir rock was deposited during the Cretaceous over the Yucatan Slope and is divided into Upper, Middle, and Lower Cretaceous. The main reservoir rocks are carbonate debris flow facies in the Upper Cretaceous. The formation was diagenetically altered by dolomitization, dissolution, and fracturing processes. All these processes were related to a compressional tectonic regime. Dolomitization in this area is a major control on porosity. When dolomitization exists porosity is improved and is divided into three kinds: matrix, fracture and vug porosity. Fracture and vug porosities are the main productive porosities because they increase connectivity among porous voids. Dolomitization is not homogeneous in the Cretaceous rocks in the study area, which is an important difference with the major fields cited above. Dolomitization is present in the upper and middle part of the Upper Cretaceous and in the Middle Cretaceous, but not in the Lower Cretaceous. The lower part of the Upper Cretaceous is not completely dolomitized in the study area. This heterogeneity in the porosity, and consequently in the permeability, could form vertical barriers to the flow, and it could increase the mobility of fluid movement in the aquifer in the zone, creating early irruptions of water during the production of the future wells. To characterize these complex fields and plan their development, I developed an integrated workflow. The ultimate objective of this research was a 3D-cellular model that represented all the geological complexities identified in the fields through well and seismic data. The first part of this workflow described in Chapter 1, is to define the architecture and structure of the fields. The resulting structural model was supported by the interpretation of a 3D depth migrated seismic integrated data with previous studies in nearby fields describing lithofacies and stratigraphical units to subdivide the model based on lithology supported by image well-logs and core reports. In Chapter 2, I focus on the internal distribution of the dolomitized facies in the field. I evaluated different seismic attributes and selected the ones that on both time and depth-migrated best-differentiated dolomite from limestone. Then, I incorporated them into machine learning processes to identify the process that gave us a result that was closer to the expected geology in the area. In Chapter 3, I use Nuclear Magnetic Resonance (NMR) and image logs, I estimated a dual-porosity petrophysical model. This model was then used as a parameter to select a method from those proposed by other authors to estimate dual-porosity based on basic well-logs. The selected method can be applied to future wells in the area. Then, I distributed the petrophysical properties using geostatistical methods based on the lithofacies described in chapter one. I used the dolomitization trends estimated in chapter 2, as a second variable into the geostatistical process. The result was a 3D model, which identified sweet spots to locate new development wells, estimate original volumes and, make simulations of the production of the fields

Odor Classification Using Support Vector Machine

This paper discusses about the process of classifying odor using Support Vector Machine. The training data was taken using a robot that ran in indoor room. The odor was sensed by 3 gas sensors, namely: TGS 2600, TGS 2602, and TGS 2620. The experimental environment was controlled and conditioned. The temperature was kept between 27.5 0C to 30.5 0C and humidity was in the range of 65% -75 %. After simulation testing in Matlab, the classification was then done in real experiment using one versus others technique. The result shows that the classification can be achieved using simulation and real experiment

Minimally intrusive strategies for fault detection and energy monitoring

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 185-196).This thesis addresses the need for automated monitoring systems that rely on minimally intrusive sensor arrays. The monitoring techniques employed in this thesis require fewer sensors because they take a different approach to the measurement problem. Specifically, these techniques use the power distribution network in the target system as a power source, a sensor array, and a communications channel. In this "multi-use" approach, the only measurement sources are a set of centrally located electrical transducers (i.e. voltage and current sensors) and a set of remotely located sensors that communicate with a central processing unit via power line modems. In general, these systems determine the status of critical loads or systems using only electrical data. Thus, remotely located sensors are only employed in order to gather information that would be difficult, if not impossible, to obtain electrically. Examples of such quantities include air exchange rates and occupancy levels in individual rooms. This thesis describes the development and application of several critical features of the minimally intrusive monitoring systems described above. First, it presents several model-based methods that make it possible to use electrical data to detect faults in certain mechanical systems.(cont.) In particular, two such models are described. The first of these is intended to be applied in systems in which an electromechanical actuator cycles its operation according to the value of some other variable, such as a pressure or a temperature. Examples include compressed air and vacuum systems. The other model is used to diagnose the impending failure of the mechanical coupling through which a motor drives an inertial load such as a pump impeller. This thesis also describes the development of a minimally intrusive airflow monitoring system that uses ozone as a tracer gas. This system fits easily into the "multi-use" framework because it relies on a network of distributed ozone generators and detectors whose operation is coordinated via power line communications. Finally, this thesis also presents and demonstrates a method for detecting the operation of various electrical loads using transient changes in the measured line voltage. This technique makes it possible to use "plug-in" sensors to determine the operating schedule of each of the various loads in a home or commercial facility. All of the techniques and methods described here are demonstrated experimentally.by Robert Williams Cox, IV.Ph.D