Parallel numerical simulation for a super large-scale compositional reservoir

 A compositional reservoir simulation model with ten-million grids is successfully computed using parallel processing techniques. The load balance optimization principle for parallel calculation is developed, which improves the calculation speed and accuracy, and provides a reliable basis for the design of reservoir development plan. Taking M reservoir as an example, the parallel numerical simulation study of compositional model with ten million grids is carried out. When the number of computational nodes increases, message passing processes and data exchange take much time, the proportion time of solving equation is reduced. When the CPU number increases, the creation of Jacobian matrix process has the higher acceleration ratio, and the acceleration ratio of I/O process become lower. Therefore, the I/O process is the key to improve the acceleration ratio. Finally, we study the use of GPU and CPU parallel acceleration technology to increase the calculation speed. The results show that the technology is 2.4 ∼ 5.4 times faster than CPU parallel technology. The more grids there are, the better GPU acceleration effect it has. The technology of parallel numerical simulation for compositional model with ten-million grids presented in this paper has provided the foundation for fine simulation of complex reservoirs.Cited as: Lian, P., Ji, B., Duan, T., Zhao, H., Shang, X. Parallel numerical simulation for a super large-scale compositional reservoir. Advances in Geo-Energy Research, 2019, 3(4): 381-386, doi: 10.26804/ager.2019.04.0

Injection parameters optimization of crosslinked polymer flooding by genetic algorithm

The crosslinked polymer flooding, which is developed on the basis of polymer flooding, is a new type of flooding technology. As an EOR method, cross-linked polymer flooding has become a research hotspot. In the process of cross-linked polymer flooding, if the concentrations of the polymer and the crosslinking agent are small, the viscosity of the solution is low, and it will not achieve the oil displacement effect. Meanwhile, if the concentrations of the polymer and crosslinking agent are large, the viscosity of the solution is high, it needs high pressure to drive it flowing in the formation. Further, with the increasing injection of chemical agents, the contradiction between reduced production and increased cost has presented. The performance of crosslinked polymer flooding depends on the interaction of these two factors. Therefore, the concentrations of polymer and crosslinking agent should be optimized. In this paper, an optimal design method is proposed by using genetic algorithm with global optimization characteristics algorithm, combining with the chemical flooding numerical simulation software UTCHEM, the concentrations of the chemical agents are optimized. Firstly, the cumulative oil production is calculated by numerical simulation software UTCHEM, then the concentrations of the chemical agents are randomly generated by the genetic algorithm in the encoding process, and the fitness function takes the profit of cross-linked polymer flooding. Given a set of initial values, through crossover and mutation of population, optimized injection concentrations of the polymer and cross-linking agent are obtained by the multi-generational calculation.Cited as: Lian, P., Li, L., Duan, T. Injection parameters optimization of crosslinked polymer flooding by genetic algorithm. Advances in Geo-Energy Research, 2018, 2(4): 441-449, doi: 10.26804/ager.2018.04.0

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

Abstract This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization

Numerical Simulation Modeling of Carbonate Reservoir Based on Rock Type

There are many types of carbonate reservoir rock spaces with complex shapes, and their primary pore structure changes dramatically. In order to describe the heterogeneity of K carbonate reservoir, equations of porosity, permeability, and pore throat radii under different mercury injection saturations are fitted, and it shows that 30% is the best percentile. R30 method is presented for rock typing, and six rock types are divided according to R30 value of plugs. The porosity-permeability relationship is established for each rock type, and the relevant flow characteristics of each rock type have been studied. Logs are utilized to predict rock types of noncored wells, and a three-dimensional (3D) rock type model has been established based on the well rock type curves and the sedimentary facies constraint. Based on the relationship between J function and water saturation, the formula of water saturation, porosity, permeability, and oil column height can be obtained by multiple regressions for each rock type. Then, the water saturation is calculated for each grid, and a 3D water saturation model is established. The model can reflect the formation heterogeneity and the fluid distribution, and its accuracy is verified by the history matching

Application of multi-point geostatistics in deep-water turbidity channel simulation: A case study of Plutonio oilfield in Angola

In order to simulate the deep water channel distribution of the Oligocene O73 sand layers in the Plutonio oilfield in Angola of west Africa. Based on the shallow high frequency seismic data, the morphology and quantitative scale of shallow channel were studied. By analogy, this study was used as guidance for the scale statistics of single deep channel sandstone, and a three dimensional quantitative training image was created. On this basis, the deep water channel distribution was simulated using multi-point geo-statistics Snesim algorithm and tested by real drilling. The results show that the width and depth of shallow single channel are in linear correlation, while the tortuosity is negatively correlated with the slope gradient exponentially. The average depth of single channel sandstone was 13 meters and the average width was 162 meters. It is concluded that the deep water channel distribution simulation results consist with well data obtained through high resolution gradient impedance inversion, extraction of shallow channel geologic body as 3-D quantitative training image and simulation using Snesim algorithm. The spatial morphology and size of different channels are constrained by the quantitative characteristics of training image, and can reproduce geometric characteristics and spatial structure of deep water channels and levees. Key words: deep-water sedimentation, turbidity channel, multi-point geo-statistics, shallow-water sedimentation, three-dimensional training image, Oligocene, Lower Congo-Congo Fan Basi

Progress of deep learning in oil and gas reservoir geological modeling

With the rapid development of big data and deep learning based on artificial intelligence technology, reservoir geological modeling has gradually moved from traditional two-point geostatistical modeling, object-based modeling, multi-point geostatistical modeling, and sedimentary process-based modeling to intelligent geological modeling stage.Intelligent geological modeling based on deep learning mainly uses adversarial generation networks to build a 3D geological model.At present, these studies focus on the improvement of network architectures and algorithms, especially the conditioning of various types of observed data such as seismic and well logging.Few studies focus on sample data obtaining.At present, most of the training samples used in the research are synthetic data based on object modeling or sedimentary process methods.To truly apply this technology to actual underground oil and gas reservoirs, more attention needs to be paid on the acquisition of real sample data.We believe a general artificial intelligence geological modeler is the main direction in the future.However, it is difficult to achieve technological breakthroughs only by the statistical learning method of deep neural networks.The combination of statistical learning and symbol learning may be the only way to realize this technology

A study of petrophysical properties based on digital core technology: A case study of a porous carbonate reservoir in the overseas J Oilfield

Developing overseas petroleum exploration and production businesses is a necessary way to guarantee the energy security in China. However, in evaluating overseas reservoirs or designing development plans, due to lack of the first-hand data, the pore structures and seepage mechanisms cannot be well understood and the evaluation effects are influenced. In this study, with the porous carbonate reservoir of the overseas J Oilfield as a case study, the digital core technology is proposed to analyze the pore structure and seepage mechanism. ①With the thin section images of different flow units as the input data, after preprocessing of the medium filtering and threshold segmentation, the digital cores are reconstructed based on the Markov Chain Monte Carlo numerical reconstruction algorithm. ②The bore throat distribution, pore throat connectivity and porosity of the digital cores are analyzed. ③The lattice Boltzmann method is adopted to perform a single phase and two phase oil-water flow simulation in the digital cores, and the absolute permeability and relative permeability curves are calculated based on the simulation results. The reconstructed three-dimensional digital core can describe the differential characteristics of the pore throat radius distribution and pore throat connectivity of porous carbonate rocks in different flow units. The digital core porosity is highly consistent with the porosity of thin section images, and the digital core permeability shows a good positive correlation with the core permeability, thus conforming to the flow unit of the real core. The relative permeability curves of oil-water two-phase flow simulation show differences in the two-phase seepage capacity of different flow units, which can be used as the input of numerical simulation and the estimation of reservoir recovery. The results of digital core analysis are consistent with the results of physical experiments, thus verifying the reliability of the digital core analysis technique. This study provides a new strategy for reservoir evaluation and seepage study in case of data insufficiency and is valuable for reservoir description and effective development

Hierarchical constraint geological modelling method for carbonate paleokarst caves: A case study of Ordovician fracture-cavern unit in Tahe Oilfield

The characteristics of carbonate paleokarst cave and filling have always been the difficulty of characterization and modelling. Based on the seismic, geological and logging data, this paper puts forward the idea of three-level progressive modelling of karst cave complex, single karst cave and karst cave internal filling. The karst cave complex is a large-scale geological body, which can be identified by seismic in most cases. The truncated Gaussian simulation method is used to establish the karst cave complex model; the single karst cave belongs to the medium-scale geological body, and which is difficult to beidentified by seismic. Based on a large number of outcrops measured data, the single karst cave distribution model is established by using the target based indicative point process simulation method; the internal filling of karst cave is small.It is barely recognizable by seismic waves at current resolutions. The multi-point geostatistics method is applied to establish a single karst cave internal filling structure model. The "three-step" modeling idea of hierarchical constraint can gradually reduce the uncertainty of geological model, improve the accuracy of geological modelling.The new idea was tested by a case study in a fracture-cavity unit in Tahe Oilfield, and the related issues and study directions of the new idea were discussed. The test results indicate that the "three-step" modeling idea karst cave reservoir, and the implementation effect is remarkable