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_ Saltwater disposal (SWD) well operations are energy-intensive and typically lack continuous performance visibility. This article presents a results-driven case study from an ongoing collaboration between a midstream oil and gas company and Neuralix Inc., in which artificial intelligence (AI) and first principles-based time series analytics were used to deliver significant operational cost reductions. Neuralix's AI system delivered up to a 40% electricity savings across a subset of injection sites in Phase 1, with projected annualized improvements exceeding 40% once scaled. This work demonstrates how interpretability, domain specificity, and first principles thinking can unlock actionable value from complex supervisory control and data acquisition (SCADA) environments. Introduction SWD is a cornerstone of produced-water management in oil and gas operations. However, it comes with significant power costs due to high-pressure injection pumping. As a midstream operator managing several sites across Oklahoma and Texas, the client operator aimed to reduce electricity usage while improving operational oversight. Neuralix achieved this through the deployment of an AI-powered key performance indicator (KPI)-monitoring and optimization system. The collaboration sought to - Reduce kWh/bbl and cost/bbl - Identify underperforming pump configurations. - Deliver transparent, actionable insights to engineers and field teams. First Principles Approach Neuralix’s approach at solving complex operational challenges is deeply rooted in first principles thinking. Instead of relying on opaque, "black box" AI models, Neuralix breaks each challenge down into its fundamental components—physics, chemistry, and operational constraints—and builds solutions from the ground up. In the case study project this meant - Deconstructing pump energy inefficiencies to their core thermodynamic and hydraulic causes. - Designing interpretable analytics around kWh/bbl, $/bbl, and flow rate as governing KPIs. This method allowed the solution developer to pinpoint why certain pumps consume more power per bbl and what operational conditions (e.g., abrubt changes, poor filter quality, suboptimal frequencies) are driving that behavior. It is particularly critical in SWD disposal operations where SCADA data is noisy, multivariate, and often lacks labels. Technical Implementation, Data Ingestion, and Structuring Neuralix’s proprietary Data Lifecycle Templatization (DLT) system standardized ingestion of time series data from diverse SCADA systems. Core parameters included: - Motor frequency (Hz) - Flow rate (B/D) - Voltage and amperage - Pressure readings - kWh pricing integration