Intelligent reservoir modeling of Lower Huron Shale

The work presented in this thesis is an intelligent reservoir modeling and analysis of Lower Huron Shale in eastern Kentucky. Methodology used for this analysis is a recently developed Top Down Intelligent Reservoir Modeling, that couples artificial intelligence and data mining techniques with conventional reservoir engineering methods. A total of 77 wells completed in Lower Huron Shale in eastern Kentucky were used in this study. Well production data was obtained from the company operating the wells, while completion reports and well logs were downloaded from publicly available database at Kentucky Geological Surveys website. The downloaded well logs were digitized, and detailed geological interpretation of the studied area was performed. More information about the reservoir was acquired through decline and type curve analyses and single well history matching. Single well history matching was performed with publicly available shale and tight gas reservoir simulator. All of the acquired data was used in the development of spatiotemporal dataset that was further analyzed with state of the art in artificial intelligence and data mining (fuzzy pattern recognition, artificial neural networks). Finally, reservoir was divided into zones of different relative reservoir quality and full field artificial intelligence empowered predictive model of the reservoir was developed and verified

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17

Reservoir modeling of New Albany Shale

The intent of this study is to reassess the potential of New Albany Shale formation using a novel and integrated workflow, which incorporates field production data and well logs using a series of traditional reservoir engineering analyses complemented by artificial intelligence & data mining techniques. The model developed using this technology is a full filed model and its objective is to predict future reservoir/well performance in order to recommend field development strategies.;The impact of different reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas production in New Albany Shale have been presented.;The study was conducted using publicly available numerical model, specifically developed to simulate gas production from naturally fractured reservoirs.;The study focuses on several New Albany Shale (NAS) wells in Western Kentucky. Production from these wells is analyzed and history matched. During the history matching process, natural fracture length, density and orientations as well as fracture bedding of the New Albany Shale are modeled.;Sensitivity analysis is performed to identify the impact of reservoir characteristics and natural fracture aperture, density and length on gas production, using information found in the literature and outcrops and by performing sensitivity analysis on key reservoir and fracture parameters.;Then, the history-matched results of 87 NAS wells have been used to develop a full field reservoir model using an integrated workflow, named Top-Down, Intelligent Reservoir Modeling. In this integrated workflow unlike traditional reservoir simulation and modeling, we do not start from building a geo-cellular model. Top-Down intelligent reservoir modeling starts by analyzing the production data using traditional reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, Single-well History Matching, Volumetric Reserve Estimation and Recovery Factor. These analyses are performed on individual wells in a multi-well New Albany Shale gas reservoir in Western Kentucky that has a reasonable production history. Data driven techniques are used to develop single-well predictive models from the production history and the well logs (and any other available geologic and petrophysical data).;Upon completion of the abovementioned analyses a large database is generated. This database includes a large number of spatio-temporal snap shots of reservoir behavior. Artificial intelligence and data mining techniques are used to fuse all these information into a cohesive reservoir model. The reservoir model is calibrated (history matched) using the production history of the most recent set of wells that have been drilled in the field. The calibrated reservoir model is utilized for predictive purposes to identify the most effective field development strategies including locations of infill wells, remaining reserves, and under-performer wells. Capabilities of this new technique, ease of use and much shorter development and analysis time are advantages of Top-Down modeling as compared to the traditional simulation and modeling.;In addition, 31 recently drilled well in Christian county Western Kentucky-Halley\u27s Mills quadrangle have been used to perform Top-down modeling. Zone manager feature of Geographix software is used. The available production data are going to be the attributes in this feature. The contours are generated and the results have been compared with the result of Top-down modeling (Fuzzy pattern recognition). Structural map, isopach map and the other geological map has been generated using Geographix.;Additionally, in order to indentify the effect of horizontal lateral length on well productivity from New Albany Shale, fracture network has been regenerated in order to represent the distribution of natural fracture in that formation

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Production History Matching and Forecasting of Shale Assets Using Pattern Recognition

Generating long-term development plans and reservoir management of shale assets has continued apace. In this study, a novel method that integrates traditional reservoir engineering with pattern recognition capabilities of artificial intelligence and data mining is applied in order to accurately and efficiently model fluid flow in shale reservoirs. The methodology is efficient due to its relatively short development time and is accurate as a result of high quality history matches it achieves for individual wells in a multiwell asset. The technique that is named Artificial Intelligence (AI) Based Reservoir Modeling is a formalized and comprehensive, full-field empirical reservoir model. It integrates all aspects of shale reservoir development from well location and configuration to reservoir characteristics and to completion and hydraulic fracturing. This approach not only has a much faster turnaround time compared to the numerical simulation techniques, but also models the production from the field with good accuracy, incorporating all the available data. This integrated framework enables reservoir engineers to compare and contrast multiple scenarios and propose field development strategies. AI-based Modeling is applied to a Marcellus Shale asset that includes 135 horizontal wells from 43 pads with different landing targets. The full field AI-based Shale model is used for predicting the future well/reservoir performance, forecasting the behavior of new wells/pads and to assist in planning field development strategies. Furthermore, this study takes advantage of applying advanced pattern recognition tools in order to investigate the impact of design and native parameters on gas production as well as optimizing the completion and stimulation parameters for newly planned wells

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS