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Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, impact of geomechanical parameters and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific and hydrocarbon rich formations.In this paper we discuss using a new and completely different approach to modeling, history matching, forecasting and analyzing oil and gas production in shale reservoirs. In this new approach instead of imposing our understanding of the flow mechanism and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. In other words, by carefully listening to the data from wells and the reservoir we developed a data driven model and history match the production process from Shale reservoirs. The history matched model is used to forecast future production from the field and to assist in planning field development strategies. We use the last several months of production history as blind data to validate the model that is developed. This is a unique and innovative use of pattern recognition capabilities of artificial intelligence and data mining as a workflow to build a full field reservoir model for forecasting and analysis of oil and gas production from shale formations. Examples of three case studies in Lower Huron and New Albany shale formations (gas producing) and Bakken Shale (oil producing) is presented in this paper.

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In this paper we propose Universal trace co-kriging (UTrCoK), a novel methodology for interpolation of multivariate Hilbert space valued functional data. Such data commonly arises in multi-fidelity numerical modeling of the subsurface and it is a part of many modern uncertainty quantification studies. Besides theoretical developments we also present methodological evaluation and comparisons with the recently published projection based approach by Bohorquez et al. [2016]. Our evaluations and analyses were performed on synthetic (oil reservoir) and real field (Uranium contamination) subsurface uncertainty quantification case studies. Monte Carlo analyses were conducted to draw important conclusions and to provide practical guidelines for all future practitioners.

In this paper a fast track reservoir modeling and analysis of the Lower Huron Shale in Eastern Kentucky is presented. Unlike conventional reservoir simulation and modeling which is a bottom up approach (geo-cellular model to history matching) this new approach starts by attempting to build a reservoir realization from well production history (Top to Bottom), augmented by core, well-log, well-test and seismic data in order to increase accuracy. This approach requires creation of a large spatial-temporal database that is efficiently handled with state of the art Artificial Intelligence and Data Mining techniques (AI & DM), and therefore it represents an elegant integration of reservoir engineering techniques with Artificial Intelligence and Data Mining. Advantages of this new technique are a) ease of development, b) limited data requirement (as compared to reservoir simulation), and c) speed of analysis.All of the 77 wells used in this study are completed in the Lower Huron Shale and are a part of the Big Sandy Gas field in Eastern Kentucky. Most of the wells have production profiles for more than twenty years. Porosity and thickness data was acquired from the available well logs, while permeability, natural fracture network properties, and fracture aperture data was acquired through a single well history matching process that uses the FRACGEN/NFFLOW simulator package. This technology, known as Top-Down Intelligent Reservoir Modeling, starts with performing conventional reservoir engineering analysis on individual wells such as decline curve analysis and volumetric reserves estimation. Statistical techniques along with information generated from the reservoir engineering analysis contribute to an extensive spatio-temporal database of reservoir behavior. The database is used to develop a cohesive model of the field using fuzzy pattern recognition or similar techniques. The reservoir model is calibrated (history matched) with production history from the most recently drilled wells. The calibrated model is then further used for field development strategies to improve and enhance gas recovery.

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The difficulty in applying traditional reservoir simulation and modeling techniques for unconventional reservoir forecasting makes the use of statistical and modern machine learning techniques a relevant proposition for shale systems. However, the most current applications of these techniques often ignore the systematic time variations in production decline rates. Traditional decline curve modeling techniques fall short in predicting actual physical parameters and are highly dependent on the assumed physical model.In this paper, we propose a non-parametric statistical approach, more specifically, using a modern technique termed functional data analysis (FDA). In FDA production data is modeled as a time series composed of a sum of weighted smooth analytical basis functions. Instead of assuming a model, this technique allows us to learn the model from the data by jointly fitting all production profiles in the field. Principal component analysis can be applied to the functional data, termed (fPCA). Applying fPCA to declining rates from many wells, transforms time varying data into lower-dimensional score data. We found that in the real field data studied, 2-3 such functional principal components were sufficient to describe the entire production in a shale play. Low dimensional score data is also very valuable for data mining and sensitivities studies that enable better understanding of production drivers. Finally, building regressional relationships between geological and completions parameters and functional principal component scores enables forecasting production at new well locations with new sets of geological and completion parameters.Proposed methodology is demonstrated on a real reservoir case study with 172 horizontal and hydraulically fractured wells. Wells originate from one of the most prolific shale plays in North America, and have been in production for at least 500 days. Case study was focused on oil production rates, and obtained forecasting error over multiple test sets (100) was 21% (on average).