Seismic identification and characterization of complex storage space oil and gas reservoirs

To predict complex reservoir spaces(with developed caves,pores,and fractures),based on the results of full-azimuth depth migration processing,we adopted reverse weighted nonlinear inversion to improve the accuracy of porous reservoir prediction.Scattering imaging three-parameter wavelet transform technology was used to accurately predict small-scale cave bodies.The joint inversion method of velocity and amplitude anisotropy was developed to improve the accuracy of small and medium-sized fracture prediction.The results of multiscale fracture modeling and characterization,interwell connectivity analysis,and connection path prediction are consistent with the production condition.Finally,based on the above prediction findings,favorable reservoir development areas were predicted.The above ideas and strategies have great application value for the efficient exploration and development of complex storage space reservoirs and the optimization of high-yield well locations.

虚拟强度指数优化钻井技术的发展与应用

随着勘探开发的不断深入,新的钻井技术难题不断出现,对钻井速度的要求也越来越高。钻探过程中影响钻井速度的因素很多,尤其是钻井参数的影响最明显,因此合理选用钻井参数非常重要。通过调研不同的钻井参数优化模型,优选出了较为完善的虚拟强度指数(VSI)法。利用相关软件,实时采集转盘转速、扭矩、钻压等钻井参数,计算出VSI曲线,然后根据其变化情况及时调整钻井参数,确保钻头一直处于最佳破岩状态。现场5口井的试验应用表明,该方法能准确判断钻头在井底的工作状况,延长钻头使用寿命,实现提高钻井速度、缩短钻井周期的目标。

电缆地层测试复合流动压力转换模型

大排量电缆地层测试器在较薄储层测试时,压力波动很快传到储层上下边界,流动形态由球形流动过渡到平面径向流动,可以借鉴常规试井或钻柱地层测试(DST)产能评价理论进行产能评价,但其不同于DST测试。DST测试的井底流压为储层厚度上的平面径向流压力,而电缆地层测试获得的探头处压力为球形流压力。因此,如果想利用常规试井方法获得储层产能,就必须将探头的压力转换成等效的平面径向流压力。针对该问题,利用渗流力学方法推导了球形流-径向流压力转换模型,可将电缆地层测试探头处的球形流压力转换成等效的径向流井底流压,进而通过不同稳定测试流量下的压差-流量数据进行产能评价,为大排量电缆地层测试实现产能评价提供了必要的理论基础,从而拓展了电缆地层测试的功能。

Multi-resolution graph-based clustering analysis for lithofacies identifi cation from well log data: Case study of intraplatform bank gas fi elds, Amu Darya Basin

In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.

Classification of tight sandstone reservoirs based on NMR logging

The traditional reservoir classification methods based on conventional well logging are inefficient for determining the properties,such as the porosity,shale volume,J function,and flow zone index,of the tight sandstone reservoirs because of their complex pore structure and large heterogeneity.Specifically,the method that is commonly used to characterize the reservoir pore structure is dependent on the nuclear magnetic resonance(NMR)transverse relaxation time(T2)distribution,which is closely related to the pore size distribution.Further,the pore structure parameters(displacement pressure,maximum pore-throat radius,and median pore-throat radius)can be determined and applied to reservoir classification based on the empirical linear or power function obtained from the NMR T2 distributions and the mercury intrusion capillary pressure ourves.However,the effective generalization of these empirical functions is difficult because they differ according to the region and are limited by the representative samples of different regions.A lognormal distribution is commonly used to describe the pore size and particle size distributions of the rock and quantitatively characterize the reservoir pore structure based on the volume,mean radius,and standard deviation of the small and large pores.In this study,we obtain six parameters(the volume,mean radius,and standard deviation of the small and large pores)that represent the characteristics of pore distribution and rock heterogeneity,calculate the total porosity via NMR logging,and classify the reservoirs via cluster analysis by adopting a bimodal lognormal distribution to fit the NMR T2 spectrum.Finally,based on the data obtained from the core tests and the NMR logs,the proposed method,which is readily applicable,can effectively classify the tight sandstone reservoirs.

A graphical model for haloanhydrite components and P-wave velocity:A case study of haloanhydrites in Amu Darya Basin

Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.

Comparison of different inversion methods of D-T_(2)two-dimensional nuclear magnetic resonance logging and applicability analysis

D-T_(2)two-dimensional nuclear magnetic resonance(2D NMR)logging technology can distinguish pore fluid types intuitively,and it is widely used in oil and gas exploration.Many 2D NMR inversion methods(e.g.,truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization methods)have been proposed successively,but most are limited to numerical simulations.This study focused on the applicability of different inversion methods for NMR logging data of various acquisition sequences,from which the optimal inversion method was selected based on the comparative analysis.First,the two-dimensional NMR logging principle was studied.Then,these inversion methods were studied in detail,and the precision and computational efficiency of CPMG and diffusion editing(DE)sequences obtained from oil-water and gas-water models were compared,respectively.The inversion results and calculation time of truncated singular value decomposition(TSVD),Butler-Reds-Dawson(BRD),LM-norm smoothing,and TIST-L1 regularization were compared and analyzed through numerical simulations.The inversion method was optimized to process SP mode logging data from the MR Scanner instrument.The results showed that the TIST-regularization and LM-norm smoothing methods were more accurate for the CPMG and DE sequence echo trains of the oil-water and gas-water models.However,the LM-norm smoothing method was less time-consuming,making it more suitable for logging data processing.A case study in well A25 showed that the processing results by the LM-norm smoothing method were consistent with GEOLOG software.This demonstrates that the LM-norm smoothing method is applicable in practical NMR logging processing.

Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis

Logging facies analysis is a significant aspect of reservoir description.In particular,as a commonly used method for logging facies identification,Multi-Resolution Graph-based Clustering(MRGC)can perform depth analysis on multidimensional logging curves to predict logging facies.However,this method is very time-consuming and highly dependent on the initial parameters in the propagation process,which limits the practical application effect of the method.In this paper,an Adaptive Multi-Resolution Graph-based Clustering(AMRGC)is proposed,which is capable of both improving the efficiency of calculation process and achieving a stable propagation result.More specifically,the proposed method,1)presents a light kernel representative index(LKRI)algorithm which is proved to need less calculation resource than those kernel selection methods in the literature by exclusively considering those"free attractor"points;2)builds a Multi-Layer Perceptron(MLP)network with back propagation algorithm(BP)so as to avoid the uncertain results brought by uncertain parameter initializations which often happened by only using the K nearest neighbors(KNN)method.Compared with those clustering methods often used in image-based sedimentary phase analysis,such as Self Organizing Map(SOM),Dynamic Clustering(DYN)and Ascendant Hierarchical Clustering(AHC),etc.,the AMRGC performs much better without the prior knowledge of data structure.Eventually,the experimental results illustrate that the proposed method also outperformed the original MRGC method on the task of clustering and propagation prediction,with a higher efficiency and stability.

Serial structure multi-task learning method for predicting reservoir parameters

Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.

Analysis of the elemental spectral characteristics of single elemental capture spectrum log using Monte-Carlo simulation

In this study,the gamma-ray spectrum of single elemental capture spectrum log was simulated.By numerical simulation we obtain a single-element neutron capture gamma spectrum.The neutron and photon transportable processes were simulated using the Monte Carlo N-Particle Transport Code System(MCNP),where an Am–Be neutron source generated the neutrons and thermal neutron capture reactions with the stratigraphic elements.The characteristic gamma rays and the standard gamma spectra were recorded,from analyzing of the characteristic spectra analysis we obtain the ten elements in the stratum,such as Si,Ca,Fe,S,Ti,Al,K,Na,Cl,and Ba.Comparing with single elemental capture gamma spectrum of Schlumberger,the simulated characteristic peak and the spectral change results are in good agreement with Schlumberger.The characteristic peak positions observed also consistent with the data obtained from the National Nuclear Data Center of the International Atomic Energy Agency.The neutron gamma spectrum results calculated using this simple method have practical applications.They also serve as an reference for data processing using other types of element logging tools.

Well Logging Stratigraphic Correlation Algorithm Based on Semantic Segmentation

Well logging curves serve as indicators of strata attribute changes and are frequently utilized for stratigraphic analysis and comparison.Deep learning,known for its robust feature extraction capabilities,has seen continuous adoption by scholars in the realm of well logging stratigraphic correlation tasks.Nonetheless,current deep learning algorithms often struggle to accurately capture feature changes occurring at layer boundaries within the curves.Moreover,when faced with data imbalance issues,neural networks encounter challenges in accurately modeling the one-hot encoded curve stratifi cation positions,resulting in signifi cant deviations between predicted and actual stratifi cation positions.Addressing these challenges,this study proposes a novel well logging curve stratigraphic comparison algorithm based on uniformly distributed soft labels.In the training phase,a label smoothing loss function is introduced to comprehensively account for the substantial loss stemming from data imbalance and to consider the similarity between diff erent layer data.Concurrently,spatial attention and channel attention mechanisms are incorporated into the shallow and deep encoder stages of U²-Net,respectively,to better focus on changes in stratifi cation positions.During the prediction phase,an optimized confi dence threshold algorithm is proposed to constrain stratifi cation results and solve the problem of reduced prediction accuracy because of occasional layer repetition.The proposed method is applied to real-world well logging data in oil fi elds.Quantitative evaluation results demonstrate that within error ranges of 1,2,and 3 m,the accuracy of well logging curve stratigraphic division reaches 87.27%,92.68%,and 95.08%,respectively,thus validating the eff ectiveness of the algorithm presented in this paper.