An Agentic Framework for Causal Discovery and Forecasting in Oil and Gas Time Series

Industrial time-series analysis requires both accurate forecasting and actionable explanations, particularly in production systems where interventions propagate with delays across interconnected assets. We present an ongoing applied project that develops an agentic framework integrating time-series causal discovery, temporal dependency analysis, and foundation-model forecasting into a unified workflow for operational diagnosis and decision support. Engineers interact through a conversational interface to (i) localize operational events and regime changes, (ii) estimate causal links and time lags among injector and producer variables, and (iii) generate short- and medium-horizon forecasts using a time-series foundation model (TimesFM). We demonstrate the approach on realistic simulated oil and gas production datasets generated with Unisim, capturing injector--producer interference patterns. Early observations indicate that combining causal structure with foundation-model forecasting improves interpretability and supports faster investigation than forecasting-only baselines, while maintaining competitive predictive performance. We summarize practical lessons from integrating causal tools and forecasting into an engineer-facing workflow and outline next steps toward multi-scenario causal discovery and interactive deployment.