Abstract: A method for converting seismic data into geophysical property models. The method includes receiving seismic data from a site, wherein the seismic data includes seismic stacks and well logs received from the site. A large-scale structural model (LSSM) is then constructed from the received seismic data. The method includes estimating initial geophysical properties related to the site using a first neural network trained by the seismic data and the constructed LSSM. Data may then be extracted from the received data and used to train a second neural network. The method also includes revising the initial geophysical properties using a second trained neural network. The revised geophysical properties, which may include p-velocity, density, or s-velocity, may be displayed on a screen. The method also includes performing a site action based on the revised geophysical properties.

Abstract: A method includes receiving baseline data representing an area corresponding to a first time. receiving first measurement data representing a first portion of the area corresponding to a second time subsequent to the first time. training a machine learning model to reduce an influence of a non-repeatability factor based on a combination of the baseline data and the first measurement data. receiving second measurement data representing a second portion of the area corresponding to the second time. the second portion of the area including a feature of interest that was not present at the first time or that has changed between the first and second times. and modifying the second measurement data using the machine learning model to remove the non-repeatability factor in the second measurement data.

Abstract: An integrated workflow is presented including a suite of data-driven technologies that aims to substantially reduce the cost of monitoring data acquisition, improve the robustness and efficiency of time-lapse data processing procedures to shorten the turnaround time of projects utilizing seismic data for monitoring sub-surface fluid reservoirs. In particular, plumes of subsurface CO2 may be monitored, including CO2 deliberately injected into the sub-surface as a sequestration technique. The workflow may include two parts: (1) cost-effective data acquisition schemes and (2) efficient data processing algorithms. The technology components in the workflow may include deep learning sparse monitoring data reconstruction and optimal acquisition survey design, deep learning deblending of simultaneous source monitoring data, time-lapse data repeatability enforcement through deep learning, and rapid CO2 plume body and property estimation directly from pre-migration monitoring data.

Abstract: A process includes immersing a device in a cooling fluid, the cooling fluid comprising an alkyl modified silicone oil having the following average chemical structure (I) : (CH 3) 3SiO- [ (CH 3) 2) SiO] m- [R (CH 3) SiO] n-Si (CH 3) 3 (I) where: R in each occurrence is an alkyl or substituted alkyl having 6 or more and at the same time 17 or fewer carbon atoms; subscript m has a value of one or higher and at the same time less than 22, subscript n has a value of one or higher, and the sum of m+n is greater than 5 and at the same time less than 50.