A Diagnostic and Prognostic Framework for Integrated Reservoir-Completion Management Using Intelligent Well Data

Abstract

Abstract Well construction and maintenance represent most of the costs of a deepwater offshore field exploitation. Planning and execution times in this scenario are normally impacted by geological and operational uncertainties leading to deviation on the actual times and consequently costs. Intelligent wells have been used to face the geological uncertainties for heterogeneous reservoirs with multiple pay zones in Brazil's Pre-Salt reservoirs. Robust diagnostic and prognostic of abnormal operational situations have not been addressed completely up to now even with real-time data available from the Intelligent Wells. The completion design, cost and reliability are the basis for the identification of all sensors needed to provide a rich data set for a fit for purpose application using a system view. Even though these are permanent sensors, the idea is suitable for coiled tubing and e-line operations where additional sensing options are available, but without the capability to capture the dynamic behavior. Data quality methods as well as integrated modeling simulation are all at a very early stage. This challenge is aggravated by the huge amounts of data coming from an Intelligent Well which is far greater than the data flow that the petroleum industry normally has to deal with. Additionally, the increased complexity of the completion affects the coupled well-reservoir modeling used in today's analysis. The evaluation of the well equipment degradation is also vital to prevent future complications that will lead to unexpected well shut-ins. Flow control, well integrity, artificial lift, and their related equipment are all targets for tuning the maintenance practices. In this paper, we propose a framework which includes:real-time data quality control;non-isothermal coupled well-reservoir modeling taking into account completion equipment;appropriate analysis tools. The aim is to simultaneously optimize well completion operations and maintenance practices. We focus our strategy on: initial well completion design; formation evaluation; stimulation, and equipment reliability, discussing applications that support our proposition. Introduction One of the most representative recent oil discoveries worldwide is the Santos Basin Pre-Salt Cluster (Moczydlower et al, 2012), offshore Brazil. Considering the importance of the reserves and intending to accelerate cash flow, Petrobras and its associates decided to fast track its development. Several subsurface uncertainties resulted from this choice which leaded to a mix of classical strategies (information, flexibility and robustness) for the development plan. IW (Intelligent Well), in this context, was identified as an extra flexibility that was worth investing in. Fast response to unexpected events and better production system behavior prediction were the main issues to address.