Shale Value

Headquartered in Texas, Shale Value enables businesses to optimize shale and tight oil assets through converging machine learning and reservoir engineering principles. The company’s industry-first analysis also allows to accurately value shale assets. “We are helping operators in acquiring acreage, well location selection, facilities and drawdown optimization, and benchmarking well performance. As such, we can support our clients across the entire shale value chain. This is what enables us to stay a notch above the rest,” says Pavana Gadde, CEO, Shale Value.

To prove the efficacy of the PVT algorithm, the company has run FACET through several blind tests across several shale plays and fluid types, and every time the predictions were more than 95 percent accurate.

Operators have utilized FACET to improve their top-line and bottom-line operations. For instance, one of Shale Value’s clients was planning on traditional fluid analysis that involves field sampling, followed by expensive lab experiments and fluid modeling. Instead, they subscribed to FACET and leveraged the machine learning based models. As a result, the client could eliminate the need for sample collection, which enabled them to efficiently run everyday operations and eliminate unnecessary expenses. They saved almost $300,000 to $500,000 in sampling and lab costs. In addition, the client witnessed an uplift of 3-5 percent oil volume with zero CapEx through separator optimization.

Shale Value has also developed Forecasting through industry-leading Inferred Reservoir Modeling (FIRM) that forecasts well performance through a reservoir engineering-based analysis. The standard methods for forecasting production and valuing shale assets using public well data are too simplistic to incorporate the complexities of shale production.
Consequently, they lead to a plethora of mixed messages and significant inaccuracies in shale asset valuations. As opposed to these simplistic curve-fits, FIRM converges reservoir engineering analytical equations, reservoir simulations and machine learning to forecast well performance. “That’s why we can estimate the impact of rock, frac and fluid on well performance for each well in the play and this paves the way for meaningful engineering analysis,” says Pavana. As a result, clients can have the right information at their fingertips to benchmark and optimize their operations.