Research of Big Data Production Measurement Method for SRP Wells Based on Electrical Parameters

Abstract

Production measurement plays a vital role in the daily management of unconventional oil wells. It enables reservoir managers to gain a comprehensive understanding of reservoir changes and facilitates dynamic analysis and scientific development plans for the unconventional oil field. This paper focuses on accurately measuring well production by tracking over 300 sucker rod pumps (SRPs) in an experimental area of an oil field. The study utilizes easily obtainable continuous electrical parameters and real-time well production as training parameters. Accurate identification of the top and bottom dead points of the power curve is crucial in converting the power curve into the SRP’s dynamometer card. To achieve this, FFT is employed to extract single-period data from multi-period data. Subsequently, the top and bottom dead points are identified. The SRP electric power curve and corresponding real-time production data are segregated into samples based on the stroke cycle time, resulting in 200,000 valid samples. Deep learning techniques are then applied to classify the production state of pumping wells. FFT and statistical feature extraction are performed on the electric curve, and deep learning is utilized with the production parameters as input vectors and the well fluid production as output results. Through extensive training, a big-data-based SRP production calculation model is established, and subsequently used to calculate the production of SRPs in the experimental area of northeastern China’s oil field. The model is validated against actual production data. For low-yield wells with a daily production less than 6 m3/d, the model error remains below 0.5 m3/d. Additionally, the relative error for high-yield wells surpassing 6 m3/d stays under 10%, meeting the expectations of managers. This big data production measurement model serves as a valuable tool for operators to optimize the production system and detect oil well faults. Particularly in a low oil price environment, this method helps managers reduce costs and improve efficiency.