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

Conditional simulation of IRF-k in the petroleum industry and the expert system perspective

Geostatistical modeling of reservoir rock-properties -- Conditional simulation of intrinsic random functions of order k -- Geostatistical estimation of the effective permeability tensor in a three-dimensional petroleum reservoir -- Quantitative-numerical characterization of the crystal viking field, south-central Alberta : an integrated approach -- The expert system perspective : a theory of artificially intelligent geostatics

Genetic programming application in predicting fluid loss severity.

Numerous wells worldwide encounter significant, costly, and time-consuming lost circulation issues during drilling or while deploying tubulars across naturally fractured or induced fractured formations. This can potentially lead to formation damage, wellbore instability, and even blowouts. Effectively addressing this problem and restoring fluid circulation becomes crucial to curbing non-productive time and overall operational expenses. Although numerous methods have been introduced, a universally accepted industry solution for predicting lost circulation remains absent due to the complex interplay of various factors influencing its severity. Anticipating the onset of circulation loss is imperative to mitigate its impacts, minimise costs, and reduce risks to personnel and the environment. In this study, an innovative machine learning approach employing multigene genetic algorithms is utilised to analyse a dataset of 16,970 drilling datasets from 61 wells within the Marun oil field, located in Iran, where severe loss of circulation occurred. Geological characteristics, operational drilling parameters, and the properties of the drilling fluid were all considered. The dataset encompasses 19 parameters, of which seven are chosen as inputs for predicting lost circulation incidents. These inputs are then employed to construct a predictive model, employing an 85:15 training-to-test data ratio. To assess the model's performance, unseen datasets are utilised. The novelty of this study lies in the proposed model's consideration of a concise set of relevant input parameters, particularly real-time surface drilling parameters that are easily accessible for every well. The model attains a remarkable level of prediction accuracy for fluid loss, as indicated by various performance indices. The results indicate a mean absolute error of 1.33, a root mean square error of 2.58, and a coefficient of determination of 0.968. The suggested prediction model is optimised not only for data reduction but also for universal prediction and compatibility with other existing platforms. Moreover, it aids drilling engineers in implementing suitable mitigation strategies and designing optimal values for key operational surface parameters, both prior to and during drilling operations

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Simulation Approach Selection in Reservoir Management

Rapid evolution of technologies in petroleum industry in last decades has significantly improved our abilities in hydrocarbon reservoirs development. The number and complexity of tasks to be solved by reservoir engineers are gradually increasing, while the cost of field development projects is rising. In this conditions, optimal decision-making in reservoir management becomes critical since it might result in either significant benefit or financial loss to a production company. Although a significant improvement was made in project risk management to control project costs in the case of unfavorable outcome, reservoir evaluation still plays the important role and affect entire reservoir management and production process. Since the work of petroleum engineers actively involves reservoir simulation and target search for optimal solution of the particular reservoir assessment problems, selection of the most appropriate simulation approach in a timely manner is important. Successful search for suitable solution to a particular reservoir engineering problem is always a non-trivial task since it involves analysis and processing of large amounts of data and requires professional expertise in the subject area. In this work we proposed an expert system, what provide flexible framework for the proper simulation approach selection and involves thorough data analysis, multiple constraints handling, expert knowledge utilization, and intelligent output requirements implementation. This expert system utilizes linguistic method of the pattern recognition theory for knowledge base design and inference engine implementation, what significantly simplifies procedures of the system design and provides it with tuning flexibility. This thesis elaborates on major aspects of the expert system design in close relation to data processing and recommended solution finding methods. To validate the expert system’s applicability, several tests were designed based on the synthetic Brugge field case and real petroleum reservoir data. These tests demonstrate functionality of the major expert system elements and advantages of selected implementation methods. Based on obtained results we can conclude successful development of the expert system for appropriate simulation approach selection

Implications of Computational Cognitive Models for Information Retrieval

This dissertation explores the implications of computational cognitive modeling for information retrieval. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010). The two major topics of this dissertation are desirability and information scent. Desirability is the context independent probability of an item receiving attention (Recker & Pitkow, 1996). Desirability has been widely utilized in numerous experiments to model the probability that a given memory item would be retrieved (Anderson, 2007). Information scent is a context dependent measure defined as the utility of an information item (Pirolli & Card, 1996b). Information scent has been widely utilized to predict the memory item that would be retrieved given a probe (Anderson, 2007) and to predict the browsing behavior of humans (Pirolli & Card, 1996b). In this dissertation, I proposed the theory that desirability observed in human memory is caused by preferential attachment in networks. Additionally, I showed that documents accessed in large repositories mirror the observed statistical properties in human memory and that these properties can be used to improve document ranking. Finally, I showed that the combination of information scent and desirability improves document ranking over existing well-established approaches

Application of Simple Smart Logic for Waterflooding Reservoir Management

A simple smart logic for controlling inflow control valves (ICV) in waterflooding reservoir management is implemented and analyzed, with the final objective of improving the long term financial return of a petroleum reservoir. Such a control is based in a reactive simple logic that responds to the watercut measured in the ICV. Basically, when the watercut increases, the ICV is set to close proportionally. For comparison purposes, four strategies are presented: base case scenario with conventional control, the best completion configuration found by trial-and-error, the reactive control, and a deterministic optimal control based on Nonlinear Gradient Method with adjoint-gradient formulation is shown for comparison purposes. Finally, all four strategies are tested again in different reservoir realizations in order to mimic the geological uncertainties. Two different synthetic reservoir models were studied. First, a simple cube with a five-spot well configuration, in which the permeability field has a horizontal pattern defined by lognormal distributions. The second model is a benchmark proposed by the Dutch university, TU delft, with 101 channelized permeability fields representing river patterns. For the first model, no significant relative gain is found neither in the variable control nor in the optimal control. Manly because of the high homogeneity of the reservoir models. Therefore, no intelligent completion is recommended. On the other hand, for the second and more complex case, the results indicate an expressive relative gain in the use of simple reactive logic. Besides, this type of control achieves results nearly as good as the optimal control. The test in different realizations, however, shows that reservoir characterization is still a key part of any attempt to improve production. Although the variable reactive control is semi-independent, with action being taken based on measurements, some parameters need a priori model to be tuned