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Results
Abstract This paper presents a workflow that evaluates whether the production rate decline or abnormal pressure behavior is attributed to formation damage. The workflow uses artificial neural networks (ANN) as the engine behind the evaluation process. All the training and validation data comes from a reservoir simulator. The required parameters that are used for building the neural networks are the completion factor (RF) and skin. These parameters are then used to match the production and pressure profiles of the subject well. Formation damage refers to the reduction of the effective permeability near the wellbore. There are several causes of formation damage including but not limited to the completion and workover fluid, fines movement, and scale deposition. In this paper, the effect of plugged perforation sets and skin are examined. If the reservoir simulation model has a suitable history match, the model response should not considerably deviate from the real well response. If that deviation occurs, the developed ANN is introduced to check if that deviation is a result of formation damage. The reservoir simulator is used to generate several data sets with variable completion factors (RF) and skin parameters to be used for training and testing the ANN. The RF and skin can vary through time to reflect the actual changes in production response. The ANN then outputs the RF and skin factor based on the observed pressure and production profiles. The recommended output of the ANN is eventually validated with the reservoir simulator. This workflow has been tested on a synthetic, heterogeneous reservoir model. Results show good prediction capabilities for the developed ANN in terms of producing formation damage parameters that correspond the simulated data. The workflow also suggests which parameter (the RF factor or skin) should be updated in the reservoir simulation model to reflect the observed production response. The results indicate that the data for the subject wells in the reservoir simulation model can be updated in a fast and efficient way using ANN. The ability to vary the number of layers, number of neurons, learning rate, and training algorithm makes the ANN a suitable tool for tackling problems where the relationship between parameters is difficult to comprehend.
- Asia > Middle East > Saudi Arabia (0.46)
- Africa > Nigeria (0.29)
Abstract This paper presents a workflow to validate the hydraulic fracture properties in the reservoir simulator after the fracture job has been completed. The workflow incorporates artificial neural networks (ANN) as the engine behind the validation process. The required hydraulic fracture parameters that need to be fed to the reservoir simulator are mainly the fracture half-length, fracture permeability, and fracture width. These parameters constitute the fracture conductivity and are used to match the production and pressure profiles after the fracture job is completed. If the reservoir simulation model has a satisfactory history match, the model response after the fracture job should not drastically deviate from the observed well response. If that deviation occurs, the developed ANN is introduced to help in figuring out a set of fracture conductivity parameters that match the observed data. The process starts by running the reservoir simulator to generate several data sets with variable fracture conductivity parameters to be used for training and testing the ANN. The ANN then outputs the fracture conductivity parameters based on the observed pressure and production profiles. The suggested output of the ANN is eventually validated with the reservoir simulator to check for the results accuracy. This workflow has been theoretically tested on a synthetic reservoir model with heterogeneous reservoir properties. A fracking job of up to five stages is performed on wells in a low permeability oil reservoir. Results show the ANN is able to reproduce the fracture conductivity parameters with up to 84% accuracy when the stimulated well has been in production for one year. This number rises to 96% when additional year of production data is collected. The availability of different training algorithms makes the artificial neural networks a fast and reliable tool to analyze the relationships between various parameters. As the results of this paper suggest, the reservoir simulation model can be updated with the aid of ANN and without compromising the captured physics during the history match.
- North America > United States (0.94)
- Asia > Middle East > Saudi Arabia (0.47)