Summary Enhanced oil recovery (EOR) in fractured carbonate reservoirs is challenging because of the heterogeneous and oil-wet nature. In this work, a new application of using polymer nanospheres (PNSs) and diluted microemulsion (DME) is presented to plug fractures and enhance water imbibition to recover oil from the tight, naturally fractured carbonate reservoirs. DME with different electric charges is compared through contact-angle and core-imbibition tests to evaluate their performances on EOR. The cationic DME is chosen because it has the fastest wettability-alteration rate and thus the highest oil recovery rate. Migration and plugging efficiency tests are conducted to identify the screened particle sizes of PNSs for the target reservoir cores. PNSs with a particle size of 300 nm are demonstrated to have the best performance of in-depth propagation before swelling and plugging after swelling within the naturally fractured cores are used in this study. Then coreflooding experiments are conducted to evaluate the EOR performance when PNSs and DME are used together, and results indicate that the oil recovery rate is increased by 24.3 and 44.1% compared to using PNSs or DME alone. In the end, a microfluidic experiment is carried out to reveal how DME works with PNSs.
Acidizing in un-fractured carbonate reservoirs has been well studied through modeling and simulation. Since carbonate reservoirs are often naturally fractured, fractures should be modeled for realistic acidizing operations. We present adaptive enriched Galerkin (EG) methods to simulate acidizing in fractured carbonate reservoirs. We adopt a two-scale continuum model for the acid transport. The coupled flow and reactive transport systems are spatially discretized by EG methods. Fractures are introduced using local grid refinement (LGR) technique. Adaptive mesh refinement (AMR) is implemented around wormhole interfaces. Simulation results show that acidizing in fractured carbonate reservoirs is largely dependent on the fracture system while acidizing in unfractured carbonate reservoirs is mainly determined by operation parameters such as acid injection rate. Computationally, the proposed EG scheme has less numerical dispersion and grid orientation effects than standard cell center finite difference/volume methods. AMR is very efficient to track the wormhole growth and speed up acidizing simulations.
This paper developed a new modeling method of the point bar internal architecture of meandering river reservoir based on a new meander migration process inversion algorithm and virtual geo-surfaces automatic fitting technology. Firstly, we proposed a new inversion simulation algorithm of meander migration process based on the river hydrodynamics which can backward simulate the migration centerlines of channel at each time step starting from the centerline of the terminal abandoned channel. Considering the fact that lateral accretion interlayers have the same strike with the ancient channel where they draped, through extracting restored channel centerlines at very high density combined with 3D geometry characterization, the 3D virtual geo-surfaces of lateral accretion interlayers inside the point bar were built. After examining the extent of match-up between each virtual geo-surfaces and well data in 3D space, the virtual geo-surfaces which don't conform to well data were removed and the virtual geo-surfaces which conform to both well data and predefined rules were retained as the final geo-surfaces model of real lateral accretion interlayers. In order to test the reliability of this method, we applied this method to study the reservoir architecture of the west region of NmI3 depositional unit of M oilfield in Bohai Bay Basin, East China. M oilfield is one of the biggest oilfield in Bohai Bay Basin, East China. A total of 140 wells penetrated the NmI3 depositional unit. The average well spacing is about 400m. Constrained by the well spacing and the size of the channel and the point bar, we only characterize the 4th order architecture unit by means of interwell 3D correlation. And the lateral accretion interlayers model was built using the inversion simulation method. Based on the internal architecture model of the point bar, we explained the big difference of fluid production rate between 2 prodution wells which are in the same point bar and have roughly the same distance to the injection water well. The results show that the the injected water flows along the strike of lateral accretion interlayers, suggesting an advantageous flow path was formed inside the point bar. So the relative spatial position between wells and lateral accretion interlayers is a key factor that influences production dynamic. This rule can be used to guide well placement and waterflooding optimization in point bar reservoir.
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.
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