Application of artificial neural networks for lithofacies determination based on limited well data

Lithofacies definition in the subsurface is an important factor in modeling, regardless of the scale being at reservoir or basin level. In areas with low exploration level, modeling of lithofacies distribution presents a complicated task as very few inputs are available. For this purpose, a case study in the Požega Valley was selected with only one existing well and several seismic sections within an area covering roughly 850 km2. For the task of expanding the input data set for lithofacies modeling, neural network analysis was performed that incorporated interpreted lithofacies (sandstone, siltite, marl, and breccia-conglomerate) in a single well and attribute data gathered from a seismic section. Three types of different neural networks were used for the analysis: multilayer perceptron, radial-basis function, and probabilistic neural network. As a result, three lithofacies models were built alongside a seismic section based upon predictions acquired from the neural networks. Three lithofacies were successfully predicted on the section while the breccia-conglomerate was either missing or underpredicted and mostly positioned in a geologically invalid interval. Results obtained by single networks differed from one another, which indicated that a result from a single network should not be treated as representative; thus, the facies distribution for modeling should be acquired from either an ensemble of neural networks or several neural networks. Analysis showed the initial potential of the usability of neural networks and seismic attribute analysis on vintage seismic sections with possible drawbacks of the applications being pointed out

Multi-Attribute Seismic Analysis Using Unsupervised Machine Learning Method: Self-Organizing Maps

Seismic attributes are a fundamental part of seismic interpretation and are routinely used by geoscientists to extract key information and visualize geological features. By combining different findings from each attribute, they can provide a good insight of the area and help overcome many geological challenges. However, individually analyzing multiple attributes to find relevant information can be time-consuming and inefficient, especially when working with large datasets. It can lead to miscalculations, errors in judgement and human bias. This is where Machine Learning (ML) methods can be implemented to improve existing interpretations or find additional information. ML can help by handling large volumes of multi-dimensional data and interrelating them. Methods such as Self Organizing Maps (SOM) allow multi-attribute analysis and help extract more information as compared to quantitative interpretation. SOM is an unsupervised neural network that can find meaningful and reliable patterns corresponding to a specific geological feature (Roden and Chen, 2017). The purpose of this thesis was to understand how SOM can help make interpretations of direct hydrocarbon indicators (DHI) in the Statfjord Field area easier. Several AVO attributes were generated to detect DHIs and were then used as input for multi-attribute SOM analysis. SOMPY package in Python was used to train the model and generate SOM classification results. Data samples were classified based on BMU hits and clusters in the data. The classification was then applied to the whole dataset and converted to seismic sections for comparison and interpretation. SOM classified seismic lines were compared with the results of the AVO attributes. Since DHIs are anomalous data, they were expected to be represented by small data clusters and BMUs with low hits. While SOM reproduced the seismic reflectors well, it did not define the DHI features clearly for them to be easily interpreted. Use of fewer seismic attributes and computational limitations of the machine could be some of the reasons behind not achieving desired results. However, the study has room for improvement and the potential to produce meaningful results. Improvements in model design and training, and also the selection of input attributes are some of the areas that need to be addressed. Furthermore, testing other Python libraries and better handling of large datasets can allow better performance and more accurate results

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Automatic Stratigraphic Interpretation of Oil Sand wells from well logs datasets typically involve recognizing the patterns of the well logs. This is done through classification of the well log response into relatively homogenous subgroups based on eletrofacies and lithofacies. The electrofacies based classification involves identifying clusters in the well log response that reflect ‘similar’ minerals and lithofacies within the logged interval. The identification of lithofacies relies on core data analysis which can be expensive and time consuming as against the electrofacies which are straight forward and inexpensive. To date, challenges of interpreting as well as correlating well log data has been on the increase especially when it involves numerous wellbore that manual analysis is almost impossible. This thesis investigates the possibilities for an automatic stratigraphic interpretation of an Oil Sand through statistical pattern recognition and rule-based (Artificial Intelligence) method. The idea involves seeking high density clusters in the multivariate space log data, in order to define classes of similar log responses. A hierarchical clustering algorithm was implemented in each of the wellbores and these clusters and classifies the wells in four classes that represent the lithologic information of the wells. These classes known as electrofacies are calibrated using a developed decision rules which identify four lithology -Sand, Sand-shale, Shale-sand and Shale in the gamma ray log data. These form the basis of correlation to generate a subsurface model

Shale lithofacies modeling of the Bakken Formation in the Williston basin, North Dakota

The Bakken petroleum system (Devonian-Mississippian) in the Williston basin of North Dakota and Montana in the United States, and Saskatchewan and Manitoba in Canada is one of the largest unconventional oil plays in North America. The Bakken Formation consists of three members: upper, middle, and lower. Both upper and lower members are shale (source rocks), whereas the middle member (reservoir rock) is composed of mixed lithologies, including sandstone, dolostone, and limestone. Underlying the lower Bakken shale member, the Three Forks Formation is another target for hydrocarbon exploration.;Although the middle Bakken member along with the Three Forks Formation have been the targets for horizontal drilling and hydraulic stimulation throughout the basin, several uncertainties remain, including facies variation due to depositional and diagenetic controls on mineral composition and organic matter content in the Bakken shale members, which could play a significant role in hydrocarbon generation and production. Although the Bakken shale members may look homogeneous in the appearance, they are significantly heterogeneous and complex mixture of quartz, smectite, illite, carbonate, pyrite, and kerogen in varying proportions. Improved characterization of the Bakken shale lithofacies is important to better understand depositional environment, lithofacies distribution, and their potential influence on hydrocarbon production.;The main objective of this work is to investigate vertical and lateral heterogeneities of the Bakken shale lithofacies, based on mineralogy and organic matter richness. Secondly, if the Bakken shale members are composed of different lithofacies, can they be associated with different depositional and/or diagenetic conditions, which could influence source, transportation, and preservation of organic matter and sediment in the Williston basin.;Core data (such as X-ray diffraction, X-ray fluorescence, and Total Organic Carbon content), conventional borehole geophysical logs (such as gamma, resistivity, bulk density, neutron porosity, and photo-electric factor), and advanced petrophysical logs (such as Spectral Gamma and Pulsed Neutron Spectroscopy) are used and integrated together to classify the Bakken shale lithofacies and build models of lithofacies distribution at multiple scales. Usually there are minimal core data, scattered advanced well logs, and ubiquitous conventional well log suites in a petroliferous basin, which hinders lithofacies analysis and petrophysical modeling. Therefore, a significant effort of this work is geared towards developing and applying cost-effective mathematical algorithms (such as Support Vector Machine and Artificial Neural Network etc.) and geostatistical techniques (such as Sequential Indicator Simulation) to classify, predict, and interpolate shale lithofacies with high accuracy, using conventional well log-derived petrophysical parameters from several wells.;The results show that both upper and lower Bakken shale members are vertically and laterally heterogeneous at core, well, and regional scales. Bakken shale members can be classified as five different lithofacies, in terms of mineralogy and organic matter content. Organic-rich shale lithofacies are more dominant than organic-poor shale lithofacies. It appears several factors (such as source of minerals, paleo-redox conditions, organic matter productivity, and preservation etc.) controlled the Bakken shale lithofacies distribution pattern. Silica in the Organic Siliceous Shale (OSS) lithofacies near the basin center is hypothesized to be related to the presence of biogenic silica (e.g. radiolaria), whereas the portion of OSS lithofacies near the basin margin is believed to be associated with eolian action. High organic matter content in the Organic Mudstone (OMD) lithofacies near the basin margin could be interpreted due to the presence of algal matter. The borehole geophysical, petrophysical approaches, and the 3D lithofacies modeling techniques developed in this study can be applied to detailed studies of complex shale formations and exploration of hydrocarbon resources worldwide

Vulnerability Assessment of Buildings due to Land Subsidence using InSAR Data in the Ancient Historical City of Pistoia (Italy)

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and European Funds for Regional Development (FEDER) under projects AQUARISK (ESP2013-47780-C2-2-R) and TEMUSA (TEC2017-85244-C2-1-P) and STAR-EO (TIN2014-55413-C2-2-P). The first author shows gratitude for the PhD student contract BES-2014-069076. The work was conceived during the research stay of P. Ezquerro and R. Tomás in the Università degli Studi di Firenze and the research stay of G. Herrera in the IGOT Lisbon University, supported by the Spanish Ministry of Education, Culture and Sport under fellowships EEBB-I-18-13014, PRX17/00439 and PRX19/00065, respectively. The S-1 monitoring activity is funded and supported by the Tuscany Region under the agreement “Monitoring ground deformation in the Tuscany Region with satellite radar data.” The authors also gratefully acknowledge TRE ALTAMIRA for having processed the S-1 data. The project was carried out using CSK® Products, © ASI (Italian Space Agency), delivered under the ASI Project Id Science 678 - “High resolution Subsidence investigation in the urban area of Pistoia (Tuscany Region, central Italy). The work is under the framework of the e-shape project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 820852. This paper is also supported by the PRIMA programme under grant agreement No 1924, project RESERVOIR. The PRIMA programme is supported by the European Union