Wednesday, October 26
This session will cover best practices and operational procedures when pumping a DFIT. It will explore how much rate and what volume to inject, how and what to monitor during the tests, and common issues and how to prevent them.
Is DFIT being used to determine information about the fracturing mechanics, the reservoir properties, or both? This session will explore the theories, concepts, and concerns of the DFIT and assess how this fundamental question affects the test design.
Ever since Nolte developed an analysis of the pressure decline signature to provide fluid efficiency for determination of fracture parameters, engineers have refined the technique by using a combination of specialized square root-t, G-function, pressure derivatives, and log-log plots. This session will show recent advances in pressure decline analysis with examples of both before and after-closure signatures in some of our more challenging formations.
This session will focus on the larger utilization of mechanical or reservoir characteristics obtained from DFIT and after closure analysis (ACA). Aspect related to production forecasting, reservoir and geology characterization, and calibration with core and logs will be explored.
Thursday, October 27
Numerical modeling of the rock mass behavior during a DFIT is done to consider the effects of critical aspects such as presence of natural fractures, local stress changes, pore pressure, poroelastic effects, mechanical properties of the rocks, fine layers, near wellbore effects and others. This session will discuss the relevance and merits of these type of analyses, and how they can help in the interpretation of DFITs.
The objective of this session is to explore injection tests techniques and best practices for vertical data wells. Vertical data wells are generally used to gain a better understanding of formation properties of potential pay and bounding intervals within a given geographical area. Presenters will demonstrate the injection techniques deployed and how they are used to validate data modeled assumptions.