Smart flare management algorithms as a mechanism to reduce associated gas flaring and reduce carbon and chemical pollution near fields

Abstract

The article addresses the problem of excessive flaring of associated petroleum gas at production sites and proposes an intelligent flare system management approach aimed at proactively reducing venting and concomitant emissions of greenhouse and toxic substances based on predictive analytics and multicriteria optimization. The empirical base comprised a de-identified corpus of process data for 2018-2022 (>50 TB) from more than 800 sensors (flow, pressure, temperature, APG composition, operating modes of compression and treatment units); a validated dynamic model of the APG gathering and utilization system was developed in MATLAB/Simulink (mean deviation of key parameters ≤3,5%), a predictive module based on an LSTM recurrent neural network for 15-minute to 6-hour horizons (MAE for APG flow 1.27 thousand m3/h at typical 80-100 thousand m3/h RMSE 2,03 thousand m3/h), and an optimization module using particle swarm optimization (PSO) that minimizes a weighted objective (flared volume, electricity for compression/transport, notional environmental penalties) by adjusting setpoints, compressor throughput, and flow distribution among consumers. Annual simulation and quasi-experimental comparison with a standard reactive strategy yielded an 87,4% reduction in flared volume (148,72 18,74 million m³/year) with proportional reductions in CO2-equivalent emissions, as well as an 8+-fold decrease in mean annual ground-level SO2 and NOx concentrations; correlation - regression analysis confirmed that under traditional control the key drivers of increased flaring are total liquid-phase throughput (r=0.893), first-stage separator pressure (r=0.761), and limited utilization of the compressor fleet (r=-0.684). The economic assessment shows high profitability: with CAPEX of $3,25 million and OPEX of $2,15 million/year, net annual cash flow was $17,35 million/year, payback period 1.87 years, and 5-year NPV (10%) $62.58 million. It is concluded that deploying smart control algorithms is both technologically and financially justified, and their scaling within a unified digital field architecture is warranted.