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Formation pore structure and reservoir parameters change continually during waterflooding due to sand production, clay erosion, and pressure/temperature variation, which causes great challenge in geological modeling and simulation. In this work, the XA Oilfield, a block with more than 20 years' waterflooding history, is used as an example to better understand the fundamental evolution mechanisms of reservoir pore network characteristics over long time waterflooding. We performed a large number of core analyses and experiments to obtain formation parameters (e.g., permeability, porosity, relative permeability, and etc.) at different development stages. The comparison illustrates that reservoir permeability can not only decrease with clay plugging, but also increase by the detachment of fine particles and even the destruction of microscopic structure. We also observed that the point/line contacts among grains decreases, the pore network connectivity increases, the clay content reduces and the rock trends to be more hydrophilic with increasing water injection. Moreover, we developed a pore network model to simulate the variation of formation parameter. The model parameters are also compared and analyzed to get a qualitative understanding of the evolvement laws, which will provide a useful guidance for reservoir accurate modeling.Keywords: Reservoir parameter variation, permeability, water flooding, pore network model, core analysis.Citation: Wang, S., Han, X., Dong, Y., et al. Mechanisms of reservoir pore/throat characteristics evolution during long-term waterflooding.

A modified ASP flooding technology, in which polymer and alkali-surfactant solution are injected alternatively for several turns, rather than being injected simultaneously as the traditional way, is studied by both lab experiments and a field test. Experimental results show that the viscosity of polymer solution will be reduced by 30% if alkalis and surfactants are added, while the dynamical interfacial tension is much higher in ASP-oil-water system than in AS-oil-water system. To give full play of these three chemicals on oil recovery, we alternatively inject polymer and alkali-surfactant slugs, and this modified ASP flooding is named A/S-P alternating flooding. A pilot test is then carried out in a water flooded reservoir since November 2008, which is employed in a block of 28 injection wells and 40 production wells. In the pilot test, alkali-surfactant slugs and polymer slugs are alternatively injected for five turns after a leading polymer slug. Injecting pressure increases in each polymer slug and decreases in the following A/S slug. In the following polymer slug, the injecting pressure of test area is 1.5MPa lower than contrast area and the injection rate per well is 9m3/d higher, while the production is 18% more. Profile tests show that the proportion of injectable thickness to total effective thickness keeps 80% or higher during the whole flooding process of the test area, while the proportion of contrast area is only 70% in the late stage. The minimum water cut during A/S-P alternating flooding is 82.0%, which is 6.1% lower than ASP flooding. However, the water cut increases significantly when A/S slugs are injected, which is an important weakness of A/S-P flooding. When chemical flooding is over, 17.8% of the original oil in place (OOIP) has been exploited by A/S-P alternating flooding, which is more than 7% higher than the contrast area flooded by ASP, while the total chemical cost is 11.3% lower. Therefore, A/S-P alternating flooding can be a cost effective enhanced oil recovery technology. A/S-P alternating flooding has more injection and production, displaces more zones, gets a higher oil recovery and uses fewer chemicals, which can be a cost effective technology.

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Screw pumps have been widely used in many oilfields to lift the oil from wellbore to ground. The pump failure and delayed repair means well shut and production loss. A deep learning model is constructed to quickly identify the working status and accurately diagnose the failure types of the screw pumps, which can help the workers always get the information and give a fast repair. Firstly, running parameters of the screw pump, such as electric current, voltage, and instantaneous rate of flow, are obtained through the Real-time Data Acquisition System. Then the correlations between values or trends of those parameters and working status of the screw pump are calculated or analyzed. Results show that there is a good correlation between the current characteristics and various working status of screw pump. Current data at different times are expressed in polar coordinates, with the polar diameter representing the current value and the polar angle representing the time. The current-time curves of massive oil wells are then plotted in images with fixed resolution and divided into nine different groups to correspond to nine frequent working status of screw pump. A convolutional neural network (CNN) model is initialized, with the current-time curve as its input and the number codes representing working status as its output. Images mentioned above are used to train the CNN model, and the model parameters, such as the number of convolution layers, the size of convolution kernels and the activation function are optimized to minimize the training losses, which are the differences between the output codes and the right codes corresponding to the images. Finally, a robust CNN model is established, which can quickly and accurately judge the working state of the screw pump through electric current data. Based on this model, a software system connected with the oilfield database is developed, which can obtain the running parameters of the screw pumps in real time, identify their working states, judge the fault types of the abnormal situations, give alarms, and put forward solution suggestions. The system has now been widely used in Shengli Oilfield, which can help staff know the working conditions and fault types of abnormal wells in real time, speed up the maintenance progress, shorten the pump shutdown time and improve the production.