05FORCE Draege Automated fluid substitution from non linear regression
Channel: Force Seminars
Date: 2018-10-02

Anders Dræge andd@statoil.com, Equinor
Title:
Automated fluid substitution from machine learning
Abstract:
Fluid substitution can be described as the process where elastic rock properties and density are known for a given fluid, and is used to calculate theoretical properties for the same rock with another fluid in the pores. Fluid substitution is essential in reservoir monitoring and hydrocarbon exploration. Gassmann theory has been the standard fluid substitution method for rock physicists, petrophysicists and reservoir engineers for more than half a century. In addition to require insight in rock physics or related disciplines, fluid substitution of large amounts of wells is time consuming. This study presents a new concept for fluid substitution that relies on machine learning to provide robust and simple fluid substitution with approximately the same or better precision as Gassmann theory. The method is called “ROck physics Fluid Substitution” (ROFS) and integrates machine learning and rock physics. ROFS allows for automatized and rapid fluid substitution of large datasets with no requirement of competence in rock physics or related disciplines . The workflow of the new method consists of the following steps when substituting a gas saturated rock to brine filled: 1) Calibrate an appropriate rock physics model to a dataset. This is done automatically by using machine learning algorithms that relies on interior – reflective Newton method for optimization. 2) Use the calibrated rock physics model from step 1) to estimate effective rock velocities with original fluid saturation (in this case gas). 3) Use new fluid properties (in this case brine properties) with the calibrated rock physics model from step 1) to calculate new velocities. 4) Calculate the velocity difference of the calibrated model with new (brine, step 3) and original (gas, step 2) fluid. 5) Apply the velocity difference from step 4) on the original logged Vp to calculate final velocities for the fluid substituted rock. All steps of this method are completely automatized, and it will be demonstrated how it can be used on datasets consisting on more than 500 wells. Results will be compared with manually performed Gassmann modelling on the same wells.