Abstract: Methods for analyzing subsurface process data in order to perform one or more subsurface operations in a subsurface are provided. Generating subsurface models is typically a long and laborious process in which subsurface process data is analyzed in order to generate the subsurface models. In contrast, work in generating the subsurface models may be front-loaded by first using a physics simulator in order to generate a training set of subsurface forward models, and then performing machine learning using the training set to generate one or more proxy models, such as a forward proxy model and an inverse proxy model. The machine learning may be constrained using physics-based rules to better converge on the proxy models. In this way, the already-trained inverse proxy model may input the subsurface process data in order to generate potential inverse models, which may then be used to perform subsurface operations in the subsurface.

Abstract: Methods of monitoring a geometric property of a hydraulic fracture within a subsurface region, wells that perform the methods, and storage media that direct computing devices to perform the methods provided. The methods include repeatedly measuring, at a plurality of measurement times, fiber strain as a function of position along a length of an optical fiber. The optical fiber is positioned within a wellbore that extends within a subsurface region and the repeatedly measuring is performed during a change in the geometric property of the hydraulic fracture. For a given measurement time of the plurality of measurement times, the methods also include differentiating the fiber strain as the function of position to generate a strain differential as a function of position along the length of the optical fiber. The methods further include determining the geometric property of the hydraulic fracture based, at least in part, on the strain differential.