This work aims to present a new paradigm in the Exploration & Production (E&P) segment using Artificial Intelligence for rheological mapping of produced fluids and forecasting their properties throughout the production life cycle. The expected gain is to accelerate the process of prioritizing target fields for application of flow improvers and, as a consequence, to generate anticipation of revenue and value creation. Rheological data from laboratory analyses of water-in-oil emulsions from different production fields collected over the years are used in a machine learning framework, which enables a modeling based on supervised learning. The Artificial Intelligence infers the emulsion viscosity as a function of input parameters, such as API gravity, water cut and dehydrated oil viscosity. The modeling of emulsified fluids uses correlations that, in general, do not represent the viscosity emulsion suitably. Currently, an improvement over empirical correlations can be achieved via rheological characterization using tests from onshore laboratories, which have been generating a database for different Petrobras reservoirs over the years. The dataset used in the artificial intelligence framework results in a machine learning model with generalization ability, showing a good match between experimental and calculated data in both training and test datasets. This model is tested with a great deal of oils from different reservoirs, in an extensive range of API gravity, presenting a suitable mean absolute percentage error. In addition to that, the result preserves the expected physical behavior for the emulsion viscosity curve. Consequently, this approach eliminates frequent sampling requirements, which means lower logistical costs and faster actions in the decision making process with respect to flow improvers injection. Moreover, by embedding the AI model into a numerical flow simulation software, the overall flow model can estimate more reliably production curves due to better representation of the rheological fluid characteristics.

This work describes a comprehensive approach to tackle systemic failure in gas lift valves in pre-salt wells. Failure analyses in gas lift valves were performed after unexpected early failures leading to tubing-annulus communication. Understanding the root causes of this problem generates value for assets, increasing equipment life, preventing unnecessary workover, and reducing costs. Suspect failed valves are systematically removed from the wells, usually by slick-line workovers, and brought to an onshore workshop, where their integrity and mechanical functionality can be analyzed. The valve's run life, equipment model and manufacturer, annular fluid, flow through the gas lift valve, operational pressure and temperature, composition of reservoir fluids and solids deposition were verified. Besides, transient simulations were carried out to provide insights on the root causes of the failure. Also, a good understanding on how each valve works, including its engineering design, was necessary to thoroughly understand the failure process. The study of gas-lift injection valves early failure in pre-salt wells have been an excellent way to understand the life cycle of production wells before the need to start lift gas injection. That leads to a comprehensive understanding about the effects of the fluids left in annulus and have supported Petrobras in most effectively managing of well integrity and workover costs. The analysis incorporates the impact of oil production, water cut, completion type, annular fluid composition, anti-scaling fluid injection (composition and efficiency) and the differential pressure between the tubing of the annulus in the valve failure model. The composition of the deposit found inside the valves and the production history of the well were essential to assemble the puzzle of how the failure mechanism works. With the acquired knowledge, it has been possible to apply barriers to avoid future events of unwanted tubing-annulus communication arising from gas-lift valve failures. This article provides a methodology and examples for a most effective understanding of the gas-lift valves failure mechanisms and their root causes, which proved to be a valuable tool for the artificial lift design and for the planning of well operations. That has contributed to maximize equipment life, cost reduction and, at last, generating value for the company.