Polymer surfactant is a unitary flooding system which be developed in recent years. It has dual characteristics of polymer and surfactant. It has strong solvency even at a lower concentration. It has low molecular weight, counter-mass, high viscosity, anti-shearing force, anti-biodegradation, anti-oxidation degradation, temperature-resistance and salt resistance, and it fits different permeability reservoir. It is also be used to under different mineral degree condition reservoir and be injected with waste water directly. For the situation which not only has the serious invalid injection and production, but also has the oil layer of remaining oil enrichment, the test area carried out comprehensive research on polymer surfactants’ profile modification to improve the development effect of thick oil pay. In this paper, the viscosity increasing property, stability, viscoelatic property, absorption characteristic, interfacial tension, flow characteristic and molecular coil size of the polymer surfactants is studied through laboratory test. The mathematical model of active constituent qualitative change of polymer surfactants flooding is established. The oil displacement scheme of polymer surfactants flooding in test area is optimized. The latest site implementation shows that polymer surfactants has a good suitability for tap potential in thick oil layer, the cumulative oil has increased 5978.5t, and predicting the enhanced oil rate is 1.7 points.

Reserves are the basis of oilfield development, and determination of reserves accurately has great significance for future adjustment of oil fields development. Due to distinct characteristics of offshore oilfield development, it is difficult to obtain dynamic reserves accurately using conventional methods. To overcome the limitations of existing methods, in this paper, a new model for calculating dynamic reserves is established through theoretical derivation with innovative introduction of relative permeability ratio and the quadratic power function of water saturation based on Buckley-Leveret function and frontal movement equation. The result shows that this approach is simple to use and with good applicability, and only needs typical oil field dynamic data. The results of the study facilitates better recalculation of oil field reserves and implementation of oilfield adjustment well. More importantly, it has laid out a new methodology for increasing oil production and exploration in Bohai oil field and provided theoretical foundation for subsequent oilfields’ efficient development.

Oilfield A is a fractured buried hill reservoir in Bohai bay of China. In order to solve the difficult problem of water flooding timing and method in oilfield. Considering the characteristics of the buried hill fractures with stress sensitivity and strong heterogeneity, the ECLIPSE software was used in the research, and a three-dimensional injection-production numerical model for horizontal wells in buried hill reservoirs is established. According to the main research factors in water flooding, a series of water flooding schemes are designed, and the optimization of water flooding timing, oil recovery rate and water flooding mode in buried hill reservoirs were carried out. The results show that the optimum pressure level of fractured reservoir is about 70% of the original reservoir pressure. The optimal water flooding method is the conventional water flooding in the initial stage, when the water cut reaches 80%, it is converted into periodic water flooding. The oil recovery is the highest when the water injection period is 4 months. Field tests show that conventional water flooding is carried out in the initial stage of the oilfield A when the pressure is reduced to 70% of the original. Periodic water flooding is carried out when water cut is 80%. Good development results had been achieved in the 10 years since oilfield A was put into production. The average productivity of single well reached 300 m 3 /d in the initial stage, at present, the water cut is 60%, and the recovery degree is 18.5%, which is better than that of similar oilfields. This technology improves the water flooding effect of blocky bottom water fractured dual media reservoirs in metamorphic buried hills, and provides a reference for the development of similar reservoirs.

The water flooding characteristic curve method is one of the essential techniques to predict recoverable reserves. However, the recoverable reserves indicated by the existing water flooding characteristic curves of low-amplitude reservoirs with strong bottom water increase gradually, and the current local recovery degree of some areas has exceeded the predicted recovery rate. The applicability of the existing water flooding characteristic curves in low-amplitude reservoirs with strong bottom water is lacking, which affects the accurate prediction of development performance. By analyzing the derivation process of the conventional water flooding characteristic curve method, this manuscript finds out the reasons for the poor applicability of the existing water flooding characteristic curve in low-amplitude reservoir with strong bottom water and corrects the existing water flooding characteristic curve according to the actual situation of the oilfield and obtains the improvement method of water flooding characteristic curve in low-amplitude reservoir with strong bottom water. After correction, the correlation coefficient between $$\frac{{k_{ro} }}{{k_{rw} }}$$ k ro k rw and $$S_{w}$$ S w is 95.92%. According to the comparison between the actual data and the calculated data, in 2021/3, the actual water cut is 97.29%, the water cut predicted by the formula is 97.27%, the actual cumulative oil production is 31.19 × 104t, and the predicted cumulative oil production is 31.31 × 104t. The predicted value is consistent with the actual value. It provides a more reliable method for predicting low-amplitude reservoirs' recoverable ability with strong bottom water and guides the oilfield's subsequent decision-making.

Owing to a lengthy oil-bearing interval, strong anisotropism, and significant difference in the fluid properties of the sandstone oil reservoir in P Oilfield, it is quite challenging to accurately the productivity of the oil well at the initial stage. In this study, a deep neural network model is established, based on a gradient boosting algorithm, XGBoost, to forecast the initial productivity of oil wells, followed by an evaluation of the main controlling factors of productivity. One hundred oil wells in the study area were divided into training and verification groups. With a specific productivity index of an oil well with a stable period of approximately 6 months at the initial production stage as the target data, and geological, engineering, and oil reservoir parameters as input data, hyper-parameters for adjustment and optimization were selected, and a deep-learning-based unconsolidated sandstone productivity forecast model was established to forecast the initial productivity of oil wells in the target area. The mean square root error of the forecast result was <0.15, which is highly consistent with actual productivity. Finally, by adopting the XGBoost algorithm, the weight ranking of the controlling factors of productivity was clarified as follows: microscopic pore structure parameter > crude oil viscosity > median grain size > lithology index > well completion method > flow zone indicator. Machine learning has the advantages of effective forecasting of oil well productivity and the main controlling factors using multiple dimensions and big data.