Well-Completion System Supported by Machine Learning Maximizes Asset Value

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197573, “Asset Value Maximization Through a Novel Well-Completion System for 3D Time-Lapse Electromagnetic Tomography Supported by Machine Learning,” by Paolo Dell’Aversana, Raffaele Servodio, and Franco Bottazzi, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed. In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection. The variations of the electromagnetic fields caused by changes of the fluid distribution are measured in a wide range of distances from the well. The data are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a machine-learning (ML) platform. The complete paper clarifies the details of the ML work flow applied to electrical resistivity tomography (ERT) models using an example based on synthetic data. Introduction An important question in well completions is how one may acquire data with sufficient accuracy for detecting the movements of the fluids in a wide range of distances in the space around the production well. One method that is applied in various Earth disciplines is time-lapse electrical resistivity. The operational effectiveness of ERT allows frequent acquisition of independent surveys and inversion of the data in a relatively short time. The final goal is to create dynamic models of the reservoir supporting important decisions in near-real-time regarding production and management operations. ML algorithms can support this decision-making process. Methodology In a time-lapse ERT survey [often referred to as a direct-current (DC) time-lapse survey], electrodes are installed at fixed locations during monitoring. First, a base resistivity data set is collected. The inversion of this initial data set produces a base resistivity model to be used as a reference model. Then, one or more monitor surveys are repeated during monitoring. The same acquisition parameters applied in the base survey must be used for each monitor survey. The objective is to detect any small change in resistivity, from one survey to another, inside the investigated medium. As a first approach, the eventual variations in resistivity can be retrieved through direct comparison between the different inverted resistivity models. A different approach is called difference inversion. Instead of inverting the base and monitor data sets separately, in difference inversion, the difference between the monitor and base data sets is inverted. In this way, all the coherent inversion artifacts may be canceled in the difference images resulting from this type of inversion. Repeating the measurements many times (through multiple monitor surveys) in the same area and inverting the differences between consecutive data sets results in deep insight about relevant variations of physical properties linked with variations of the electric resistivity.