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.

In the current age of declining oil prices, mature fields matter today more than ever. About 70% of the current world oil production is from mature fields. To unlock the remaining potential from these fields, new wells need to be implemented by utilising technological advancements in reservoir characterisation, well engineering and reservoir engineering. This paper focuses on improving reservoir understanding by using rock physics and seismic inversion technology on a mature field, which is located in offshore Sarawak Malaysia. Seismic data can be incorporated into an integrated workflow of predicting rock properties by utilising inversion processes that transform seismic data into a quantitative rock property, a descriptive measure of the reservoir. In this paper, an integrated workflow is adopted and it involves the application of rock physics driven seismic inversion for acoustic impedance (AI) prediction and geobody extraction. The extracted geobodies describe the areal extent of oil reservoir and changes in the rock property. The field being studied consists of multi-stacked channelised reservoirs containing substantial amount of crevasse splay sands. The main producing units are thin oil sands with initial gas caps. These reservoirs typically display a high degree of vertical and lateral heterogeneity. Seismic inversion and geobody prediction were used as a method for the prediction of petrophysical properties including porosity, Net-to-Gross (NTG), water saturation and permeability at geo-cellular scale. To this end, the information from different fault blocks was integrated for the simulation study and incorporated into the evaluation of the oil potential in this mature field. A holistic approach that comprises both a material balance study and a simulation study was adopted. The material balance study was used to verify the drive mechanism and to evaluate the transmissivity between different fault blocks. Prolific reservoirs were identified on inverted AI data and integrated into the static model for volumetric estimations and further high grading of the development drilling locations. As seismic inversion offers non-unique solutions, the integration of well log data allowed a technically acceptable answer as well as an estimation of the associated uncertainty. It can be concluded that rock physics driven seismic inversion results reduces uncertainty in property estimation during reservoir modelling while simultaneously improving field development plans. This paper focuses on how the integration of seismic and well data was used to optimally characterise an oil reservoir in a mature field development and create a fit-for- purpose reservoir model. The goal was to optimise reservoir characterisation and production management which leads to success and low risk re-development. Ultimately, the economics were improved through reducing the uncertainties in monetising the remaining by-passed oil reserves to a manageable range.

History Matching (HM) is one of the critical steps for dynamic reservoir modelling to establish a reliable predictive model. Numerous approaches have emerged over the decades to accomplish a robust history matched reservoir model ranging from the classical reservoir engineering approach to the widely accepted 3D numerical simulation approach and its variations. As geological complexity of the oil and gas field increases (multilayered reservoirs, heavily faulted) compounded with completion complexity (dual strings, commingle production), building a fully representative predictive reservoir model can be arduous to almost impossible task. Artificial Intelligence (AI) and machine learning has advanced almost all major industries, including the petroleum industry in general and reservoir engineering. The objective of this paper is to outline a novel approach in history matching using a data-driven approach through Artificial Intelligence via Artificial Neural Network (ANN) and Data-Driven Analytics. In this paper, a step by step methodology in building a reservoir model and history matching process using ANN will be described which includes data preparation and data QA/QC, spatiotemporal database formulation, reservoir model design, ANN architecture design, model training and history matching strategy. A case study of the implementation to Field "A" in Malaysian waters is presented where good to fair history matching quality was obtained for all production parameters. Field "A" is a 25kmx75km oil sandstone reservoirs of a highly geologically complex field (more than 200 major and minor faults, more than 30 reservoir layers) of more than 25 years of production. The challenges of history matching of this field does not only lie on its geologically complex structure and its corresponding subsurface uncertainties, but also on the production strategy of the wells that involved commingled dual strings production with several integrity issues that adds additional dimensions to the field's complexities. To date, Field "A" has no field wide history matched reservoir model using conventional numerical simulation method available due to the complexity of history matching. This long history matching woe is mitigated via the implementation of AI based reservoir model and Data Analytics. This novel approach is estimated to be more time and cost-efficient compared to the conventional method. The comparison of this AI based reservoir model and history matching methodology with the conventional numerical reservoir model approach will be discussed. Furthermore, the advantages, limitations and areas of improvements of this AI based history matching methodology will also be highlighted. The target audience of this paper would be to reservoir engineering practitioners and dynamic model simulators who is interested to learn the complementary or alternative approach in reservoir modelling apart from conventional numerical modelling in order to create time-efficient reservoir model and reducing the risks in their field development plans.

The success of extracting the best value from mature heterogeneous fields to meet global energy challenges is directly linked to innovation and creativity. The development of these fields requires optimized economic models and fit for purpose reservoir depletion strategies. Petroleum geoengineering is the answer to evaluating remaining opportunities and managing the key uncertainties using smart technologies and reducing the risk of development. This paper describes the field example of how a petroleum geoengineering based approach can optimize the fast track development of a marginal fault block within a complex oil field which is located in offshore Sarawak Malaysia. The advantages of this workflow compared to the conventional field development plan (FDP) approach is that the iterative time consumed in matching the dynamic model and adjusting the geological model has been properly managed. In our case study, this workflow has improved the quality of the technical proposal as well as saving project duration significantly. In accordance to the workflow, the reservoir architecture was interpreted based on seismic interpretation, geological and reservoir performance understanding. Seismic Inversion derived geobodies were then identified as development targets. Better delineation of the main geological features was achieved using a seismic inversion based algorithm. By prioritizing data consistency among geoscientists, geomodellers and reservoir engineers, a fast track field development plan was developed within manageable uncertainties. Greater reliability of inversion results help to avoid perpetuating bias tendencies on data used for model calibration or quality check. The petroleum geoengineering based study for such oil reservoir shows how an integrated approach enabled time saving for subsurface development concept identification covering range of estimated hydrocarbon volumes. As a result of geoengineering approach in marginal fault block, the technical proposal was completed within 8 months duration.