The Capacitance-Resistance Model (CRM) has been a useful physics-based tool for obtaining production forecasts for decades. However, the model's limitations make it difficult to work with real field cases, where a lot of various events happen. Such events often include new well commissioning (NWC). We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching. Moreover, it can be used for selecting a new well placement during infill drilling. To make the workflow even more versatile, an improved version of CRM was used. It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients. By preliminary re-weighing and interpolating these coefficients using kriging, the coefficients for potential wells can be determined. The approach was validated using both synthetic and real datasets, from which the cases of putting new wells into operation were selected. The workflow allows a fast assessment of future well performance with a minimal set of reservoir data. This way, a lot of well placement scenarios can be considered, and the best ones could be chosen for more detailed studies.

We disclose a new -age field -scale production forecast model that handles complex treatment of wellbores during their life cycle. Predictive production models have been an object of increased interest and research for a long time due to the need for a fast tool for forecasting production rates or choosing an optimal field development scheme. The existing approaches based on the material balance equation have several limitations and are not very applicable for real objects. Full -scale reservoir modeling is relatively slow and requires large computing resources. In this paper, we propose a proxy model based on advanced capacitance-resistance approach. The model predicts multiphase flow rates based on the available historical data of field production and information about well treatments. In addition, it pro-vides preferable transmissibility trends, the presence of sealed or leaking faults, and the degree of dissipation between injector-producer well pairs. The advanced feature of the model is time-dependent weight coefficients, which have not been studied previously. They help in accounting the shut -in and workover periods and can be found during the optimization procedure simultaneously. Another feature is fast calculations due to a vectorized form of the model and application of modern optimization techniques. All these options allow modeling real oil fields with a large number of wells and a complex system of production control.

To reduce inefficient costs and environmental risks, oil companies strive to optimize the process of hydrocarbon production at all stages of field development, including geological and technical works at wells. In particular, it is important to predict fluid production with high accuracy. 3D hydrodynamic modeling is a generally accepted technique for solving this problem. It provides reliable results but requires many input data, computational resources, and time for calculations. Since the decision-making process has to be reactive, it is necessary to develop a simultaneously precise and prompt predictive instrument for quick forecasts of liquid production. The most promising tools for these purposes are proxy models based on solving the material balance equation. They adapt to the existing historical data even without PVT properties and reservoir data. Some of the most popular approaches are proxy models such as Capacitance Resistance Models (CRM). CR-type model is a material balance-based flow model, which provides preferable transmissibility trends, the presence of sealing or leaking faults with compressibility effects in consideration, and dissipation between injector-producer pairs. It is a data-driven model with adjustable time constants and interwell connectivity parameters. Before the model tuning, all parameters must be initialized with analytical or random approximations, and then they can be found by an appropriate optimization procedure. Historical-based Capacitance Models can be applied to poorly studied fields. Besides, they give an opportunity to rapidly optimize field development strategy by making calculations with different well exploitation parameters. They only require historical data of hydrocarbon production volumes, injection profiles, and bottom-hole pressure dynamics as input data. One of the main is that properties in the interwell space are estimated approximately and considered to be constant throughout the entire development history. However, this is a weak assumption in the case of including well interventions and stimulations. Thus, the main goal of this work is to adjust coefficients online to changes in well operation modes, introducing new wells or shut-in the existing ones. Since the governing equation includes the considered CRM improvement, users can perform optimization over different timespans, including "special" intervals. As a result, weighting connectivity parameters of the model can be depicted on a map of well interactions versus time.

In adaptation of reservoir models a direct gradient backpropagation through the forward model is often intractable or requires enormous computational costs. Thus one have to construct separate models that simulate them implicitly, e.g. via stochastic sampling or solving of adjoint systems. We demonstrate that if the forward model is a neural network, gradient backpropagation becomes naturally involved both in model training and adaptation. In our research we compare 3 adaptation strategies: variation of reservoir model variables, neural network adaptation and latent space adaptation and discuss to what extent they preserve the geological content. We exploit a real-world reservoir model to investigate the problem in practical case. The numerical experiments demonstrate that the latent space adaptation provides the most stable and accurate results.

This paper considers the development of a computationally fast model for simulation of multiphase flow in porous media for a heterogeneous reservoir with the unlimited number of wells characterized by a different type of completion. This fast solution has been obtained by means of replacing the differential equation governing the flow in porous media by approximate governing equations which are parametrized by convolutional neural networks. The matching of the dynamic properties of the original and reduced models is ensured by conservation of spatial invariance property of the equations. The suggested approach is characterized by the minimal number of limitations and shortcomings related to geological-hydrodynamical structure and size of the original model. Also, there is no necessity of additional model training for reservoirs not included in a training dataset. Suggested approach has been evaluated on the synthetic benchmark test model SPE10, where a significant decrease in computational time has been demonstrated comparing to a traditional commercial reservoir simulator. Based on the results of all demonstrated test case scenarios, it could be noted that hybrid hydrodynamic modeling leads to a significant reduction in computational cost (by a factor of few hundreds), maintaining at the same time required accuracy of calculations.