Diagnosing Fracture-Wellbore Connectivity Using Chemical Tracer Flowback Data

TitleDiagnosing Fracture-Wellbore Connectivity Using Chemical Tracer Flowback Data
Publication TypeConference Paper
Year of Publication2018
AuthorsKumar, A., and M. M. Sharma
Conference NameUnconventional Resources Technology Conference
Date Published07/2018
PublisherUnconventional Resources Technology Conference (URTeC)
Conference LocationHouston, TX, U.S.A., July 23-25, 2018
Other NumbersURTeC: 2902023
KeywordsFracture Diagnostics, Fracture modeling
Abstract

Existing fracture diagnostic methods such as micro-seismic monitoring and tiltmeters do not provide information about fracture connectivity to the wellbore. In this work, we present a chemical tracer flowback based fracture diagnostic method to (a) estimate the fraction of the created fracture area which is open and connected to the wellbore, and (b) understand the effect of induced un-propped (IU) fracture closure on the tracer response.

 

We conducted a reservoir simulation study to model tracer injection and flowback in a complex fracture network with the help of an effective model. The model captures the effect of fracture opening and closure due to changes in the in-situ effective stress during flowback. As the fracture pressure is reduced, fractures close over time. This directly affects the tracer response during flowback. The impact of the closure rate of induced unpropped (IU) fractures on tracer response was demonstrated through simulation results. Fracture length and permeability were lumped to define an effective connected fracture length, a parameter which correlates with production. Neural network based inverse modeling was performed to estimate effective connected fracture length using tracer data.

 

Simulation results indicate that the tracer response is dominated by the fractures which are open and connected to the wellbore. Multiple peaks in the tracer response curves can be explained by the closure of IU fractures. Fracture closure can also explain the low tracer recovery typically observed in field tests. Tracer recovery is found to be proportional to production. Based on these observations, tracer peaks and recovery parameters were selected for training the neural network for inverse modeling. The trained neural network was used to estimate the effective connected fracture length. We observed a good match between neural network prediction and the fracture parameters in the simulation.

 

We present a new method to analyze chemical tracer data which includes the effect of flow and geomechanics on tracer flowback. The proposed approach can help in estimating the degree of connectivity between the wellbore and open connected fractures.

DOI10.15530/urtec-2018-2902023