THE EFFECT OF SUPERVISED FEATURE EXTRACTION TECHNIQUES ON THE FACIES CLASSIFICATION USING MACHINE LEARNING

The widely accepted supervised machine learning classification algorithms are used for the semi-automating of the feature extraction process. In the machine learning facies classification process, each wireline log is a feature in the feature space. Since features are important in classification decisions, using suitable features improves the performance of a classification algorithm. In this study, three feature sets are compared containing the original conventional features (well-logs), and the extracted features from the unsupervised PCA and supervised FDA methods, using two classifier algorithms, namely SVM and RF. The FDA showed an improvement in the performance of facies classifiers while PCA can even deteriorate the results. An F1 score of 0.61 averaged over the available 20 folds for the combination of FDA feature extractor and RF classifier is achieved. This represents a 5% improvement in the prediction accuracy, compared to the conventional use of wells information as features with an F1 score of 0.56. Moreover, the conventional method uses all seven well-logs while with the FDA we only use three features

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to gain a deeper understanding of team learning. This work presents experimental results which illustrate the importance of heterogeneous teams of collaborative learning agents, as well as outlines heuristics which govern successful construction of teams of classifiers. A number of application domains are studied in this dissertation. One approach is focused on the effects of sharing knowledge and collaboration of multiple heterogeneous, intelligent agents (hardware or software) which work together to learn a task. As each agent employs a different machine learning technique, the system consists of multiple knowledge sources and their respective heterogeneous knowledge representations. Collaboration between agents involves sharing knowledge to both speed up team learning, as well as to refine the team's overall performance and group behavior. Experiments have been performed that vary the team composition in terms of machine learning algorithms, learning strategies employed by the agents, and sharing frequency for a predator-prey cooperative pursuit task. For lifelong learning, heterogeneous learning teams were more successful compared to homogeneous learning counterparts. Interestingly, sharing increased the learning rate, but sharing with higher frequency showed diminishing results. Lastly, knowledge conflicts are reduced over time, as more sharing takes place. These results support further investigation of the merits of heterogeneous learning. This dissertation also focuses on discovering heuristics for constructing successful teams of heterogeneous classifiers, including many aspects of team learning and collaboration. In one application, multi-agent machine learning and classifier combination are utilized to learn rock facies sequences from wireline well log data. Gas and oil reservoirs have been the focus of modeling efforts for many years as an attempt to locate zones with high volumes. Certain subsurface layers and layer sequences, such as those containing shale, are known to be impermeable to gas and/or liquid. Oil and natural gas then become trapped by these layers, making it possible to drill wells to reach the supply, and extract for use. The drilling of these wells, however, is costly. Here, the focus is on how to construct a successful set of classifiers, which periodically collaborate, to increase the classification accuracy. Utilizing multiple, heterogeneous collaborative learning agents is shown to be successful for this classification problem. We were able to obtain 84.5% absolute accuracy using the Multi-Agent Collaborative Learning Architecture, an improvement of about 6.5% over the best results achieved by Kansas Geological Survey with the same data set. Several heuristics are presented for constructing teams of multiple collaborative classifiers for predicting rock facies. Another application utilizes multi-agent machine learning and classifier combination to learn water presence using airborne polar radar data acquired from Greenland in 1999 and 2007. Ground and airborne depth-soundings of the Greenland and Antarctic ice sheets have been used for many years to determine characteristics such as ice thickness, subglacial topography, and mass balance of large bodies of ice. Ice coring efforts have supported these radar data to provide ground truth for validation of the state (wet or frozen) of the interface between the bottom of the ice sheet and the underlying bedrock. Subglacial state governs the friction, flow speed, transport of material, and overall change of the ice sheet. In this dissertation, we focus on how to construct a successful set of classifiers which periodically collaborate to increase classification accuracy. The underlying method results in radar independence, allowing model transfer from 1999 to 2007 to produce water presence maps of the Greenland ice sheet with differing radars. We were able to obtain 86% accuracy using the Multi-Agent Collaborative Learning Architecture with this data set. Utilizing multiple, heterogeneous collaborative learning agents is shown to be successful for this classification problem as well. Several heuristics, some of which agree with those found in the other applications, are presented for constructing teams of multiple collaborative classifiers for predicting subglacial water presence. General findings from these different experiments suggest that constructing a team of classifiers using a heterogeneous mixture of homogeneous teams is preferred. Larger teams generally perform better, as decisions from multiple learners can be combined to arrive at a consensus decision. Employing heterogeneous learning algorithms integrates different error models to arrive at higher accuracy classification from complementary knowledge bases. Collaboration, although not found to be universally useful, offers certain team configurations an advantage. Collaboration with low to medium frequency was found to be beneficial, while high frequency collaboration was found to be detrimental to team classification accuracy. Full mode learning, where each learner receives the entire training set for the learning phase, consistently outperforms independent mode learning, where the training set is distributed to all learners in a team in a non-overlapping fashion. Results presented in this dissertation support the application of multi-agent machine learning and collaboration to current challenging, real-world classification problems

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning

4D evolution of fluvial system and channel-fill architecture of the Cretaceous Blackhawk Formation, Wasatch Plateau, Utah: An integrated fluvial rock record analysis

Using an integrated dataset comprising outcrop, core, GPR and LiDAR data, this study targets a high-quality outcrop window of the upper Cretaceous Blackhawk Formation in the eastern Wasatch Plateau in central Utah, spanning a fairly large spatial (~30 km2 area comprising eight contiguous, and vertical cliff faces) and temporal (~4 my) range. This research provides field-validation and -calibration of a wider range of fluvial heterogeneity: 1) large-scale heterogeneity (10’s of m vertically and 100’s of m laterally), 2) intermediate-scale heterogeneity (1’s of m vertically and 10’s of m laterally), and 3) small-scale heterogeneity (10’s of cm vertically and 1’s of m laterally). These sandbody- to facies-scale heterogeneities generate potential for stratigraphic compartmentalization for analogous fluvial reservoirs and prospects. Moreover, these results specifically constitute an outcrop analog to the producing tight-gas fluvial reservoirs of the adjacent hydrocarbon-prolific Uinta and Piceance Basins of Utah and Colorado, including the giant Jonah Field of Wyoming. 3D virtual outcrop model generated from LiDAR-integration has helped in avulsion-scale (~1\u27s-10\u27s kyr) to basin-fill scale (~100\u27s kyr-1\u27s myr) fluvial sandbody organization analysis down to channel-storey level. This high-resolution analysis has brought several intriguing insights. single-storey sandbodies are preferentially attendant to clustering organization, whereas multi-lateral sandbodies (i.e. channel-belt) show compensational-prone behavior. Sandbody organization is broadly compensational for the lower Blackhawk Formation, where the floodplain facies diversity is the highest. In contrast, floodplain diversity decreases stratigraphically upward such that the upper Blackhawk Formation shows the least heterogeneous floodplain with clustering-prone sandbody organization. In the quest of differentiating autogenic from allogenic signal in dynamic systems where their interplay is complexly intertwined, this study presents two incised-valley examples, where resultant fluvial organization has been interpreted, contrary to conventional wisdom, to be preferentially modulated by a dominant controlling mechanism of autogenic forcing. In filling these incised valley deposits, each of which is up to ~15-20 m thick, the dominating behavior of substrate coal compaction as an autogenic mechanism supplanted allogenic forcing (i.e. sea-level fluctuation)

Reducing Produced Water Disposal Via Effective Treatments Methods And Re-Use: Proposed Sustainable Application For Bakken, North Dakota

It is true that the advancements in both the hydraulic frack and directional drilling technologies led to less time and a bit easier ways to develop unconventional oil and gas assets worldwide. In the Bakken North Dakota, the result of these breakthroughs and advancements in technologies are that they drastically reduce the time it takes to drill and complete a well leading to more wells (347 in 2004 to 16,300 in 2020). In 2019, the United States became the largest global crude oil producer, and the unconventional Bakken Play in North Dakota is one of the major contributors to this feat. As more wells are being drilled, more waste water are being produced. Analysis also showed early increases in water cuts even in younger (less than 3 years) wells drilled around McKenzie and Williams Counties. The concern here is that the wastewater produced by these increased oilfield activities is highly saline (~170,000 to 350,000 ppm TDS), and the most commonly used water disposal method in the Bakken Formation is deep injection into disposal wells. Notwithstanding, there are growing environmental and operational concerns about the sustainability and impacts of this approach. However, if the wastewater is efficiently treated, it could be reused in hydraulic fracturing operations or to support coal mining and irrigation activities. This research uses various method to investigate the root cause of the high volume of wastewater production in the Bakken, North Dakota and how these flow back and produced water could be treated using various novel technologies like, the advanced and improved desalination, advanced electro-oxidation and dilution methods. Lastly, the research was able to provide robust and detailed results on how the Bakken treated produced water could be transformed to good use especially as base fluids for hydraulic frack fluid formulation

Final Report for the ZERT Project: Basic Science of Retention Issues, Risk Assessment & Measurement, Monitoring and Verification for Geologic Sequestration

ZERT has made major contributions to five main areas of sequestration science: improvement of computational tools; measurement and monitoring techniques to verify storage and track migration of CO{sub 2}; development of a comprehensive performance and risk assessment framework; fundamental geophysical, geochemical and hydrological investigations of CO{sub 2} storage; and investigate innovative, bio-based mitigation strategies

Hawaii geothermal drilling guide : Circular C-126

Contents: introduction -- rules and regulations -- drilling -- casing and cementing -- blowout prevention -- well completion & testing -- monitoring and reporting -- workovers, plugging, and abandonment -- emerging technology -- illustrations -- appendices"This Geothermal Drilling Guide has been prepared at the request of the State of Hawaii Department of Land and Natural Resources to provide a single, comprehensive document that describes geothermal drilling and well-testing operations for the use of potential developers, operators, and stakeholders. This guide is intended as a general reference for common practices currently found in the geothermal industry. For site-specific well programs, detailed analysis of all available project data should be performed in order to ensure compliance with applicable federal, state, and county regulations.""Prepared by GeothermEx, Inc., a Schlumberger company."Department of Land and Natural Resources, State of Hawai

INTEGRATING SEQUENCE STRATIGRAPHY AND SEISMIC ATTRIBUTES FOR QUANTITATIVE RESERVOIR CHARACTERIZATION: A CASE STUDY OF A PLIOCENE RESERVOIR, CAMPECHE SOUND, MEXICO

An integrated workflow including analysis of seismic, core, well log and biostratigraphic data was developed and conducted to both construct a reliable geologic model and characterize a Pliocene gas reservoir which overlies the Cantarell field in the Campeche Sound, southern Gulf of Mexico.In 2003, the offshore exploratory Utan #1 well was drilled to investigate the gas potential of the Pliocene sequence. The well provided successful results from facies characterized by thin mixed siliciclastic-carbonate beds contained within a faulted rollover anticline.Campeche Sound is the most prolific Mexican oil producing province where the best fields are Mesozoic-Paleocene carbonates in structural traps. Therefore, little exploration has been focused on the overlying late Tertiary and more siliciclastic section, representing a gap in the knowledge of this part of the basin where new expectations arise for non-associated gas entrapments in a traditionally oil-producing province.Based upon development of a sequence stratigraphic framework, a new play analysis is developed where the reservoirs are identified as retrogradational shoreface parasequences sitting atop third-order sequence boundaries. Basic and advanced seismic attributes contribute to the stratigraphic interpretation and gas detection. Seismic inversion for reflectivity allowed better identification of key stratigraphic surfaces. Modeled Type-I AVO and a dimmed spectral decomposition response following structural contours provide reliability to gas discrimination and reservoir delineation. The seismic attributes will require additional support to be valuable as reservoir quality predictors.Because biogenic methane and thin sheet reservoirs define the rock-fluid system, development may be uneconomic. However, the trapped gas could be reinjected at deeper depths to improve recovery efficiency of oil in the Cantarell field.The knowledge gained from this research is an important contribution to the petroleum geology of Mexico and the Gulf of Mexico basin. It confirms the petroleum system for this Pliocene play, proposes a new play concept and provides the basis for further research in the study area.Future work is recommended to extend the regional geological mapping using the integrated methodology and play analysis developed from this dissertation. New well and seismic data focused on Neogene levels should also be obtained to improve the knowledge and assure expectations for future exploration and development strategies in these and other subtle stratigraphic gas traps of this traditional oil province