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Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Unconventional reservoirs are full of uncertainty when dealing with conventional methods of modeling and analysis. The objective of this work is to use a trained artificial intelligent (AI) model to compare actual production data to AI predicted possible well production. Data-driven models based on AI are efficient tools for optimizing the production, stimulation, and completion design of wells and can be very beneficial when determining the success or failure of wells based on production. An AI model for production predictions require both native and design parameters, which include well characteristics, completion design, and stimulation design parameters. Data from over 100 Marcellus Shale wells are used to train and test an AI model for production predictions. Feature selection algorithms are used to determine the most influential input parameters pre and post modeling for both increased model accuracy and quality assurance. Post modeling, Monte-Carlo simulation and Type Curves are used to assess the performance of each well based on the AI generated well production values. AI model generation is a very useful tool for predicting production performance of existing wells, which can be used to optimize design characteristics and reservoir production. Generating AI predictive models in fields with low amount of cases to train and test the artificial neural network require very delicate and careful considerations in order to maximize the effect and accuracy of the predictive model. This study will be able to give an underlying method of applying these artificially intelligent solutions to a complex petroleum engineering problem. The ability to correctly apply these techniques will allow for the optimization of completion and stimulation designs within complex, unconventional reservoir.