Data Driven Well Production Forecast for Improved Reservoir Management and FDP Optimization - A Case Study from a Super Giant Reservoir in Abu Dhabi

Abstract This paper aims to present an integrated approach for well forecasting that combines advanced Machine Learning (ML) techniques to provide accurate oil, gas and water predictions for existing and new wells in oil reservoirs. Well forecasting is a critical task in the oil and gas industry for optimizing production and maximizing recovery from reservoirs. Traditionally, decline curve analysis has been widely used for well forecasting, but it has limitations in complex reservoirs or wells with uncertain decline trends. The proposed approach overcomes these limitations by utilizing advanced Machine Learning techniques that leverage complex real data for improved production forecasting. By incorporating ML algorithms, the proposed approach can provide more accurate and reliable predictions, particularly in challenging reservoir conditions where traditional methods may fall short. The proposed approach for well forecasting involves several key steps. Firstly, historical data of wells, including production, choke, well head flowing pressure, producing days, injection data, well connection properties, and 3D properties, are collected and preprocessed. Next, 2D properties, such as well location, spatial coordinates, and spatial attributes, are extracted from the 3D properties of the reservoir using geospatial algorithms. Then, for existing wells an ensemble of ML supervised algorithm is trained well by well with enough historical data, and nearby wells are used to train non mature wells. For future new wells, historical data plus geological 2D properties is used to create a model per reservoir. This involves collecting and preprocessing data from nearby wells in the vicinity of the new drilling location to obtain relevant properties. Uncertainty intervals are calculated using quantiles to quantify the uncertainty in the predictions. The integrated well forecasting approach presented in this paper provides a comprehensive solution for predicting well production in oil reservoirs. We used 2 different approaches to predict oil, gas and water production depending on the availability of historical data. Our approach allowed accurate predictions in complex reservoirs where wells have no clear decline trends. The use of uncertainty intervals provides a measure of prediction uncertainty, allowing for informed decision-making.