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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.

As the mature oil fields have stepped into the high water cut stage, the remaining oil is considered as potential reserves, especially the attic oil in the inclined fault-block reservoirs. A novel assisted gas–oil countercurrent technique utilizing gas oil countercurrent (GOC) and water flooding assistance (WFA) is proposed in this study to enhance the remaining oil recovery in sealed fault-block reservoirs. WFA is applied in our model to accelerate the countercurrent process and inhibit the gas channeling during the production process. Four comparative experiments are conducted to illustrate enhanced oil recovery (EOR) mechanisms and compare the production efficiency of assisted GOC under different assistance conditions. The results show that WFA has different functions at different stages of the development process. In the gas injection process, WFA forces the injected gas to migrate upward and shortens the shut-in time by approximately 50% and the production efficiency improves accordingly. Compared with the basic GOC process, the attic oil swept area is extended 60% at the same shut-in time condition and secondary gas cap forms under the influence of WFA. At the production stage, the WFA and secondary gas cap expansion form the bi-directional flooding. The bi-directional flooding also displaces the bypassed oil and replaced attic oil located below the production well, which cannot be swept by the gas cap expansion. WFA inhibits the gas channeling effectively and increases the sweep factor by 26.14% in the production stage. The oil production increases nearly nine times compared with the basic GOC production process. The proposed technique is significant for the development of attic oil in the mature oil field at the high water cut stage.