A Novel Early Warning System of Oil Production Based on Machine Learning

Abstract

For mature oilfields which have entered into the high water cut stage, many stimulation measures are adopted in order to maintain the oil production. Those measures include drilling new wells, general measures, and strengthened measures. Even though the oil production increase when the measures conducted, it will cause different degrees of production decline in the next year. Due to the irrational composition of oil production in the matured field, abnormal production decline is becoming the primary problem for stable production. Establish an effective early warning system (EWS) is important to release production alarm and take necessary measures in advance. In this paper, the factors that can affect the abnormal decline are selected and the influence degree of different factors are compared by grey relational analysis. The machine learning was adopted to build the EWS. Three distinct forms of input data are considered to improve the prediction accuracy. By using the degree of deviation from normal as the input data for the prediction model have the highest accuracy. However basic machine learning model contains many input parameters which can't obtain easily. The number of input parameter is optimization based on the variation of accuracy under different input parameter number. In order to improve the prediction accuracy the artificial samples are added into the training process. The prediction accuracy of the final optimization model can reach 92%. According to the EWS the production condition of different reservoir is evaluated. The result reveals the possibility of the occurrence of anomalous decline in different reservoir which can effectively guide the oilfield production strategy. The EWS can be an effective tool in the oil production monitor in the mature oil field.

Machine Learning-Based Production Prediction Model and Its Application in Duvernay Formation

“…On the contrary, a small value means a weak correlation [22,23]. The advantage of grey correlation analysis is that it requires less data and calculation compared to other multi-factor analysis methods [24] (random forest, regression, etc.). The basic steps of grey relation analysis are as follows:…”

Evaluation and Improvement of Pumping Well Operating Conditions in an Oil Field Block Based on Grey Correlation Analysis

“…The operating condition of the pumping unit is generally expressed by the load-carrying capacity of the sucker rod, as shown in Eq. (11). The reductor is subjected to severe cyclic loading, and the load rate is generally taken as the maximum value in one cycle [15,16].…”

“…The best-performing model was the gradient boosting machine (GBM), which had a cross-validation accuracy of 99.98%. Ma et al [11] used the grey relation method to improve the accuracy of the prediction model for oil production of pumping wells, which has 92% prediction accuracy in the oil field application.…”