Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

Application of Secondary Logging Interpretation—Taking Yan 9 Reservoir in X Area as an Example

Logging data and its interpretation results are one of the most important basic data for understanding reservoirs and oilfield development. Standardized and unified logging interpretation results play a decisive role in fine reservoir description and reservoir development. Aiming at the problem of the conflict between the development effect and the initial interpretation result of Yan 9 reservoir in Hujianshan area of Ordos Basin, by combining the current well production performance, logging, oil test, production test and other data, on the basis of making full use of core, coring, logging, thin section analysis and high pressure mercury injection data, the four characteristics of reservoir are analyzed, a more scientific and reasonable calculation model of reservoir logging parameters is established, and the reserves are recalculated after the second interpretation standard of logging is determined. The research improves the accuracy of logging interpretation and provides an effective basis for subsequent production development and potential horizons.

Data mining and well logging interpretation: application to a conglomerate reservoir

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

Gas hydrate saturation model and equation for logging interpretation

Since gas hydrate exists in three different forms at the same time such as pore filling,particle support and separate stratification,the calculation method of hydrate saturation using traditional shaly sand formation interpretation models is equivalent to considering only the simple case that hydrate exists as pore filling,and does not consider other complex states.Based on the analysis of hydrate resistivity experimental data and the general form of the resistivity-oil(gas)saturation relationship,the best simplified formula of hydrate saturation calculation is derived,then the physical meaning of the three items are clarified:they respectively represent the resistivity index-saturation relationship when hydrate particles are completely distributed in the pores of formation rocks,supported in the form of particles,and exist in layers,corresponding quantitative evaluation method of hydrate saturation is built.The field application shows that the hydrate saturation calculated by this method is closer to that obtained by sampling analysis.At the same time,it also provides a logging analysis basis for the effective development after hydrate exploration.

A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.

Anisotropy interpretation and the coherence research between resistivity and acoustic anisotropy in tight sands

Aiming at the problem of anisotropy inversion of tight sands, a new method for extracting resistivity anisotropy from array laterolog and micro-resistivity scanning imaging logging is proposed, and also the consistency of electric and acoustic anisotropy is discussed. Array laterolog includes resistivity anisotropy information, but numerical simulation shows that drilling fluid invasion has the greatest influence on the response, followed by the relative dip angle θ and electrical anisotropy coefficient λ. A new inversion method to determine ri, Rxo, Rt and λ is developed with the given θ and initial values of invasion radius ri, flushed zone resistivity Rxo, in-situ formation resistivity Rt. Micro-resistivity image can also be used for describing the resistivity distribution information in different directions, and the electrical characteristics from micro-resistivity log in different azimuths, lateral and vertical, can be compared to extract electric anisotropy information. Directional arrangement of mineral particles in tight sands and fracture development are the intrinsic causes of anisotropy, which in turn brings about anisotropy in resistivity and acoustic velocity, so the resistivity anisotropy and acoustic velocity anisotropy are consistent in trends. Analysis of log data of several wells show that the electrical anisotropy and acoustic anisotropy extracted from array laterolog, micro-resistivity imaging and cross-dipole acoustic logs respectively are consistent in trend and magnitude, proving the inversion method is accurate and the anisotropies of different formation physical parameters caused by the intrinsic structure of tight sand reservoir are consistent. This research provides a new idea for evaluating anisotropy of tight sands.

Experimental study of gas-water elongated bubble flow during production logging

In order to improve the interpretation of production log data on gas-water elongated bubble （EB） flow in horizontal wells, a multi-phase flow simulation device was set up to conduct a series of measurement experiments using air and tap water as test media, which were measured using a real production logging tool （PLT） string at different deviations and in different mixed flow states. By understanding the characteristics and mechanisms of gas-water EB flow in transparent experimental boreholes during production logging, combined with an analysis of the production log response characteristics and experimental production logging flow pattern maps, a method for flow pattern identification relying on log responses and a drift-flux model were proposed for gas-water EB flow. This model, built upon experimental data of EB flow, reveals physical mechanisms of gas-water EB flow during measurement processing. The coefficients it contains are the specific values under experimental conditions and with the PLT string used in our experiments. These coefficients also reveal the interference with original downhole flow patterns by the PLT string. Due to the representativeness that our simulated flow experiments and PLT string possess, the model coefficients can be applied as empirical values of logging interpretation model parameters directly to real production logging data interpretation, when the measurement circumstances and PLT strings are similar.

Calculation of oil saturation in clay-rich shale reservoirs:A case study of Qing 1 Member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China

The targeted reservoir,which is referred as the first member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China,is characterized by the enrichment of clay and lamellation fractures.Aiming at the technical challenge of determining oil saturation of such reservoir,nano-pores were accurately described and located through focused ion beam scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy based on Simandoux model,to construct a 4D digital core frame.Electrical parameters of the shale reservoir were determined by finite element simulation,and the oil saturation calculation method suitable for shale was proposed.Comparison between the results from this method with that from real core test and 2D nuclear magnetic log shows that the absolute errors meet the requirements of the current reserve specification in China for clay-rich shale reservoir.Comparison analysis of multiple wells shows that the oil saturation values calculated by this method of several points vertically in single wells and multiple wells on the plane are in agreement with the test results of core samples and the regional deposition pattern,proving the accuracy and applicability of the method model.

Organic matter occurrence and pore-forming mechanisms in lacustrine shales in China

The evolution of pore structure in shales is affected by both the thermal evolution of organic matter(OM)and by inorganic diagenesis,resulting in a wide variety of pore structures.This paper examines the OM distribution in lacustrine shales and its influence on pore structure,and describes the process of porosity development.The principal findings are:(i)Three distribution patterns of OM in lacustrine shales are distinguished;laminated continuous distribution,clumped distribution,and stellate scattered distribution.The differences in total organic carbon(TOC)content,free hydrocarbon content(S_(1)),and OM porosity among these distribution patterns are discussed.(ii)Porosity is negatively correlated with TOC and plagioclase content and positively correlated with quartz,dolomite,and clay mineral content.(iii)Pore evolution in lacustrine shales is characterized by a sequence of decreasing-increasing-decreasing porosity,followed by continuously increasing porosity until a relatively stable condition is reached.(iv)A new model for evaluating porosity in lacustrine shales is proposed.Using this model,the organic and inorganic porosity of shales in the Permian Lucaogou Formation are calculated to be 2.5%-5%and 1%-6.3%,respectively,which correlate closely with measured data.These findings may provide a scientific basis and technical support for the sweet spotting in lacustrine shales in China.

A Study on Identification of Conglomerate Reservoir Parameters and Oil/Water Beds in Ke82 Well Areas of Junggar Basin

This paper is mainly about the calculation of reservoir parameters and theinterpretation method for identifying oil/water beds in Ke82 well areas of Junggar basin. It isdifficult to determine the reservoir parameters with common logging methods such as core calibrationlog because of the diversity of minerals and rocks and the complexity of pore structures in theconglomerate reservoir of Junggar basin. Optimization logging exploration is a good method todetermine the porosity by establishing the multi-mineral model with logging curves based on theintegration of geological, core and well testing data. Permeability is identified by BP algorithm ofneural network. Hydrocarbon saturation is determined by correlating Archie's and Simandouxformulas. Comparing the exploratory result and core data, we can see that these methods areeffective for conglomerate logging exploration. We processed and explained six wells in the Ke82well areas. And actual interpretation has had very good results, 85 % of which conform to welltesting data. Therefore, this technique will be effective for identifying conglomerate parameters.

Application and development trend of artificial intelligence in petroleum exploration and development

Aiming at the actual demands of petroleum exploration and development,this paper describes the research progress and application of artificial intelligence(AI)in petroleum exploration and development,and discusses the applications and development directions of AI in the future.Machine learning has been preliminarily applied in lithology identification,logging curve reconstruction,reservoir parameter estimation,and other logging processing and interpretation,exhibiting great potential.Computer vision is effective in picking of seismic first breaks,fault identification,and other seismic processing and interpretation.Deep learning and optimization technology have been applied to reservoir engineering,and realized the real-time optimization of waterflooding development and prediction of oil and gas production.The application of data mining in drilling,completion,and surface facility engineering etc.has resulted in intelligent equipment and integrated software.The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment,automatic processing and interpretation,and professional software platform.The highlights of development will be digital basins,fast intelligent imaging logging tools,intelligent seismic nodal acquisition systems,intelligent rotary-steering drilling,intelligent fracturing technology and equipment,real-time monitoring and control of zonal injection and production.

A Method of Attribute Reduction Based on Rough Set

The logging attribute optimization is an important task in the well-logging interpretation. A method of attribute reduction is presented based on rough set. Firstly, the core information of the sample by a general reductive method is determined. Then, the significance of dispensable attribute in the reduction-table is calculated. Finally, the minimum relative reduction set is achieved. The typical calculation and quantitative computation of reservoir parameter in oil logging show that the method of attribute reduction is greatly effective and feasible in logging interpretation.

A multi-mineral model for predicting petrophysical properties of complex metamorphic reservoirs:Case study of the Bozhong Depression,Bohai Sea

Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of such interpretations in complex reservoirs has hindered their widespread application,resulting in severe inconvenience.In this study,we proposed a multi-mineral model based on the least-square method and an optimal principle to interpret the logging responses and petrophysical properties of complex hydrocarbon reservoirs.We began by selecting the main minerals based on a comprehensive analysis of log data,X-ray diffraction,petrographic thin sections and scanning electron microscopy(SEM)for three wells in the Bozhong 19-6 structural zone.In combination of the physical properties of these minerals with logging responses,we constructed the multi-mineral model,which can predict the log curves,petrophysical properties and mineral profile.The predicted and measured log data are evaluated using a weighted average error,which shows that the multi-mineral model has satisfactory prediction performance with errors below 11%in most intervals.Finally,we apply the model to a new well“x”in the Bozhong 19-6 structural zone,and the predicted logging responses match well with measured data with the weighted average error below 11.8%for most intervals.Moreover,the lithology is dominated by plagioclase,K-feldspar,and quartz as shown by the mineral profile,which correlates with the lithology of the Archean metamorphic rocks in this region.It is concluded that the multi-mineral model presented in this study provides reasonable methods for interpreting log data in complex metamorphic hydrocarbon reservoirs and could assist in efficient development in the future.