| dc.description.abstract | The primary focus of this thesis is to summarize a workflow that leverages 3D printing to generate repeatable rock conductivity samples, reducing uncertainty associated with experimental results. Study results illustrate how proppant concentration impacts fracture conductivity while also demonstrating that conductivity results obtained from these 3D-printed samples are repeatable and reasonable relative to previous experimental studies.
Many previous studies have illustrated that rock surface topography and proppant characteristics are the primary informants of fracture conductivity. Given that conductivity is instrumental in determining how prolific a fractured well in a given resource is, great effort has been made to employ conductivity experiments to better understand how completion design and subsurface characteristics interact to inform conductivity. Still, isolating and evaluating rock sample characteristics systematically to form consistent conclusions given the intrinsically heterogeneous nature of subsurface rocks remains challenging.
Historically, samples for conductivity experiments are generated by fracturing downhole or outcrop rocks with tension to create realistic fracture surfaces that capture a wide range of possible surface morphologies. The surfaces created are inherently distinctive, even when using samples taken from the same block. This lack of sample repeatability complicates identification of the impact of rock characteristics on conductivity.
To overcome this challenge, 3D printing was employed as a means to produce consistent samples with well-defined surfaces to be used in conductivity experimental programs. In doing so, a set of geostatistically informed coordinates was first generated to numerically depict a rough fracture surface. The coordinates in this system were then connected to form one continuous surface before sizing and scaling the resulting model to appropriately resemble the 7-inch long, 2-inch wide conductivity sample required for experimentation. The resulting 3D model was then printed with a Digital Light Processing (DLP) 3D printer. The 3D-printed output served as a prototype to create a mold of the conductivity sample, which was, in turn, used to produce “rock” samples made of high-strength cement.
With this methodology, conductivity samples with identical surface roughness and features were created and replicated. Reproducing identical samples allows for isolation and testing of parameters such as proppant size and concentration, in addition to reducing uncertainty associated with experimental results by conducting the same experiment more than once. Fracture conductivity tests were conducted using a modified-API conductivity cell and artificial “rock” samples with varied surface topography.
This thesis aims, secondarily, to summarize progress made in making proppant transport experimental workflows more comprehensive with the help of 3D printing. Like fracture conductivity, how effectively proppant is transported through a fracture network and subsequently distributed throughout that network is a key determining factor in how prolific a fractured well in a given resource turns out to be.
The foundation of these proppant transport and distribution experiments remains largely unchanged to date. That being said, this study aims to incorporate another layer of subsurface complexity by creating a fracture network for proppant transport experimentation characterized by 3D-printed, rough-walled fracture surfaces.
The rough-wall fracture network that was printed for this study is characterized with greater correlation of features in the direction of flow. Results associated with this updated fracture network were compared to the previous surface characterization generated, printed, and analyzed by Tatman et al. (2022). Although the results garnered from the most recent fracture system are preliminary, they do offer insights into the impact that surface topography has on proppant transportation and distribution.
When employed appropriately, 3D printing can be leveraged to make a variety of stimulation experimental workflows more robust in nature. | |
