Global efforts toward environmentally safe waste disposal necessitate waste slurry injection (WSI) wells to be monitored for operational compliance, safety, sustainability, and optimization of waste disposal and vaulting capacity. Pressure transit analysis (PTA) is a comprehensive and time-consuming analysis conducted on shut-in data collected post-injection to assess reservoir response to the injection and ensure compliance. An engineering team performed PTA on over 7,000 injection batches annually, which opened a venue for AI optimization. This paper presents a physics-informed machine learning (PIML) method that enhances PTA accuracy, predicting reservoir properties to enhance WSI and waste disposal.
The methodology presented in this paper integrates standard PTA with machine learning informed by physical science and geomechanical principles. A PIML model has been developed and trained using shut-in data to enable early prediction of the behavior of hydraulic fractures and other reservoir properties prior to further injections. This model is validated against 35,000 injection batches and their corresponding PTA results from 10 wells over the past 5 years and allows for better storage capacity management and operational forecasting.
The application of the PIML model resulted in a significant improvement in estimating the formation response before injection solely based on controllable parameters that can be changed at the surface. Results indicated that the model can predict injectivity, formation stress, fracture closure pressure and time, wellhead pressure at closure, bottomhole instantaneous shut-in pressure, fracture half-length, skin, and transmissivity with high confidence and accuracy, thus enabling more informed decision-making during the disposal process.
Observations from multiple test scenarios confirmed that the model reliably estimated critical reservoir characteristics under varying conditions. Conclusions drawn from these experiments suggested that incorporating machine learning with physical science principles substantially reduced the risks associated with hydraulic fracturing during waste disposal. Additionally, the ability to predict reservoir behavior ahead of injections can lead to better compliance with safety standards and reduced adverse environmental impact or set a new benchmark for operational practices in the industry.
Through the introduction of a novel integration of PIML with conventional PTA techniques, this paper contributes new insights into the preemptive estimation of reservoir properties, potentially affecting operational protocols in underground waste disposal. The methodology and findings provide actionable intelligence that could influence future designs and operational strategies in the energy industry, enhancing both safety and efficiency.