Characterization of reservoir properties and pore structure based on micro-resistivity imaging logging: Porosity spectrum, permeability spectrum, and equivalent capillary pressure curve

According to the capillary theory,an equivalent capillary model of micro-resistivity imaging logging was built.On this basis,the theoretical models of porosity spectrum(Ф_(i)),permeability spectrum(K_(i))and equivalent capillary pressure curve(pe)were established to reflect the reservoir heterogeneity.To promote the application of the theoretical models,the Archie's equation was introduced to establish a general model for quantitatively characterizing bi,K,and pei.Compared with the existing models,it is shown that:(1)the existing porosity spectrum model is the same as the general equation of gi;(2)the Ki model can display the permeability spectrum as compared with Purcell's permeability model;(3)the per model is constructed on a theoretical basis and avoids the limitations of existing models that are built only based on the component of porosity spectrum,as compared with the empirical model of capillary pressure curve.The application in the Permian Maokou Formation of Well TsX in the Central Sichuan paleo-uplift shows that the Ф_(i),K_(i),and p_(ci) models can be effectively applied to the identification of reservoir types,calculation of reservoir properties and pore structure parameters,and evaluation of reservoir heterogeneity.

Valuable Data Extraction for Resistivity Imaging Logging Interpretation

Imaging logging has become a popular means of well logging because it can visually represent the lithologic and structural characteristics of strata.The manual interpretation of imaging logging is affected by the limitations of the naked eye and experiential factors.As a result,manual interpretation accuracy is low.Therefore,it is highly useful to develop effective automatic imaging logging interpretation by machine learning.Resistivity imaging logging is the most widely used technology for imaging logging.In this paper,we propose an automatic extraction procedure for the geological features in resistivity imaging logging images.This procedure is based on machine learning and achieves good results in practical applications.Acknowledging that the existence of valueless data significantly affects the recognition effect,we propose three strategies for the identification of valueless data based on binary classification.We compare the effect of the three strategies both on an experimental dataset and in a production environment,and find that the merging method is the best performing of the three strategies.It effectively identifies the valueless data in the well logging images,thus significantly improving the automatic recognition effect of geological features in resistivity logging images.

Logging identification method of depositional facies in Sinian Dengying Formation of the Sichuan Basin

The sedimentary facies/microfacies,which can be correlated with well logs,determine reservoir quality and hydrocarbon productivity in carbonate rocks.The identification and evaluation of sedimentary facies/microfacies using well logs are very important in order to effectively guide the exploration and development of oil and gas.Previous carbonate facies/microfacies identification methods based on conventional well log data often exist multiple solutions.This paper presents a new method of facies/microfacies identification based on core-conventional logs-electrical image log-geological model,and the method is applied in the fourth member of the Dengying Formation(Deng 4)in the Gaoshiti-Moxi area of the Sichuan Basin.Firstly,core data are used to calibrate different types of facies/microfacies,with the aim to systematically clarify the conventional and electrical image log responses for each type of facies/microfacies.Secondly,through the pair wise correlation analysis of conventional logs,GR,RT and CNL,are selected as sensitive curves to establish the microfacies discrimination criteria separately.Thirdly,five well logging response models and identification charts of facies/microfacies are established based on electrical image log.The sedimentary microfacies of 60 exploratory wells was analyzed individually through this method,and the microfacies maps of 4 layers of the Deng 4 Member were compiled,and the plane distribution of microfacies in the Gaoshiti-Moxi area of the Sichuan Basin was depicted.The comparative analysis of oil testing or production results of wells reveals three most favorable types of microfacies and they include algal psammitic shoal,algal agglutinate mound,and algal stromatolite mound,which provide a reliable technical support to the exploration,development and well deployment in the study area.

Fluid Property Discrimination in Dolostone Reservoirs Using Well Logs

The Ordovician Majiagou Formation is one of the main gas-producing strata in the Ordos Basin,China.The identification of hydrocarbon-bearing intervals via conventional well logs is a challenging task.This study describes the litholog of Ma 5(Member 5 of Majiagou Formation)dolostones,and then analyzes the responses of various conventional well logs to the presences of natural gas.The lithology of the gas bearing layers is dominantly of the dolomicrite to fine to medium crystalline dolomite.Natural gas can be produced from the low resistivity layers,and the dry layers are characterized by high resistivities.Neutron-density crossovers are not sensitive to the presences of natural gas.In addition,there are no significant increases in sonic transit times in natural gas bearing layers.NMR(nuclear magnetic resonance)logs,DSI(Dipole Sonic Imager)logs and borehole image logs(XRMI)are introduced to discriminate the fluid property in Majiagou dolostone reservoirs.The gas bearing intervals have broad NMR T2(transverse relaxation time)spectrum with tail distributions as well as large T2gm(T2 logarithmic mean values)values,and the T2 spectrum commonly display polymodal behaviors.In contrast,the dry layers and water layers have low T2gm values and very narrow T2 spectrum without tails.The gas bearing layers are characterized by low Vp/Vs ratios,low Poisson’s ratios and low P-wave impedances,therefore the fluid property can be discriminated using DSI logs,and the interpretation results show good matches with the gas test data.The apparent formation water resistivity(AFWR)spectrum can be derived from XRMI image logs by using the Archie’s formula in the flushed zone.The gas bearing layers have broad apparent formation water resistivity spectrum and tail distributions compared with the dry and water layers,and also the interpretation results from the image logs exhibit good agreement with the gas test data.The fluid property in Majiagou dolostone reservoirs can be discriminated through NMR logs,DSI logs and borehole image logs.This study helps establish a predictable model for fluid property in dolostones,and have implications in dolostone reservoirs with similar geological backgrounds worldwide.

Prediction of multiscale laminae structure and reservoir quality in fine-grained sedimentary rocks:The Permian Lucaogou Formation in Jimusar Sag,Junggar Basin

Fine-grained sedimentary rocks have become a research focus as important reservoirs and source rocks for tight and shale oil and gas.Laminae development determines the accumulation and production of tight and shale oil and gas in fine-grained rocks.However,due to the resolution limit of conventional logs,it is challenging to recognize the features of centimeter-scale laminae.To close this gap,complementary studies,including core observation,thin section,X-ray diffraction(XRD),conventional log analysis,and slabs of image logs,were conducted to unravel the centimeter-scale laminae.The laminae recognition models were built using well logs.The fine-grained rocks can be divided into laminated rocks(lamina thickness of<0.01 m),layered rocks(0.01-0.1 m),and massive rocks(no layer or layer spacing of>0.1 m)according to the laminae scale from core observations.According to the mineral superposition assemblages from thin-section observations,the laminated rocks can be further divided into binary,ternary,and multiple structures.The typical mineral components,slabs,and T2spectrum distributions of various lamina types are unraveled.The core can identify the centimeter-millimeter-scale laminae,and the thin section can identify the millimeter-micrometer-scale laminae.Furthermore,they can detect mineral types and their superposition sequence.Conventional logs can identify the meter-scale layers,whereas image logs and related slabs can identify the laminae variations at millimeter-centimeter scales.Therefore,the slab of image logs combined with thin sections can identify laminae assemblage characteristics,including the thickness and vertical assemblage.The identification and classification of lamina structure of various scales on a single well can be predicted using conventional logs,image logs,and slabs combined with thin sections.The layered rocks have better reservoir quality and oil-bearing potential than the massive and laminated rocks.The laminated rocks’binary lamina is better than the ternary and multiple layers due to the high content of felsic minerals.The abovementioned results build the prediction model for multiscale laminae structure using well logs,helping sweet spots prediction in the Permian Lucaogou Formation in the Jimusar Sag and fine-grained sedimentary rocks worldwide.

Bedding parallel fractures in fine-grained sedimentary rocks:Recognition,formation mechanisms,and prediction using well log

Core,thin section,conventional and image logs are used to provide insights into distribution of fractures in fine grained sedimentary rocks of Permian Lucaogou Formation in Jimusar Sag.Bedding parallel fractures are common in fine grained sedimentary rocks which are characterized by layered structures.Core and thin section analysis reveal that fractures in Lucaogou Formation include tectonic inclined fracture,bedding parallel fracture,and abnormal high pressure fracture.Bedding parallel fractures are abundant,but only minor amounts of them remain open,and most of them are partly to fully sealed by carbonate minerals(calcite)and bitumen.Bedding parallel fractures result in a rapid decrease in resistivity,and they are recognized on image logs to extend along bedding planes and have discontinuous surfaces due to partly-fully filled resistive carbonate minerals as well as late stage dissolution.A comprehensive interpretation of distribution of bedding parallel fractures is performed with green line,red line,yellow line and blue line representing bedding planes,induced fractures,resistive fractures,and open(bedding and inclined)fractures,respectively.The strike of bedding parallel fractures is coinciding with bedding planes.Bedding parallel fractures are closely associated with the amounts of bedding planes,and high density of bedding planes favor the formation of bedding parallel fractures.Alternating dark and bright layers have the most abundant bedding parallel fractures on the image logs,and the bedding parallel fractures are always associated with low resistivity zones.The results above may help optimize sweet spots in fine grained sedimentary rocks,and improve future fracturing design and optimize well spacing.

How high can fracture porosity become in the ultra-deep subsurface?

Knowledge of how high can fracture porosity become in the ultra-deep burial conditions is important but remains problematic.Fracture aperture and porosity are measured using X-ray computed tomography(CT)at atmospheric pressure and then calculated by image logs.Special attention is paid to how high fracture porosity can become in ultra-deep(>6000 m)settings,and which situations will result in high fracture porosities.In situ stress magnitudes,which can be calculated using well logs,control fracture performances,and dissolution along fracture improve fracture porosity at ultra-deep burial depths.Low horizontal stress difference(Dr<25 MPa),very high fracture density will result in a high fracture porosity.Fracture porosity can keep as high as 2.0%in relatively low in situ stress conditions even at ultra-deep burial depths.In intense in situ stress conditions(Dr>45 MPa),a high degree of dissolution along the fracture dramatically increases fracture porosity.Dissolution will result in the vuggy fracture planes and improve fracture porosity up to 2.0%.The results provide insights into the detection,characterization,and modeling of subsurface fractures.

基于无线局域网络的远程操控系统设计及应用

为了实现石油测井地面系统的远程化操控,研究了一种基于无线局域网络的远程操控系统。提升了石油测井仪器检查时的效率,规避了沟通不畅而造成的仪器损坏。通过全新的地面系统架构连接和设置,实现了远程化操控。使得工程师更能直观看到仪器的工作状态,判断仪器的性能,同时在市场推广向客户进行设备的运行演示时,可实现地面操控、仪器反馈、技术解说现场一体化运行。

Fracture permeability estimation utilizing conventional well logs and flow zone indicator

Characteristics of the natural open fractures on the oil and gas reservoirs is crucial in drilling and production planning. Direct methods of fractures studies such as core analysis and image log interpretation are usually not performed in all drilled wells in a field. Therefore, in absence of these data, the indirect methods can play an important role. In this study, an integrated algorithm is introduced to identify the fractures and estimate its permeability employing conventional well logs. First, open fractures were identified and their properties including density, aperture, porosity and permeability were estimated using FMI log. Subsequently, the fracture index log (FR_Index) was estimated utilizing conventional logs including density, micro-resistivity, sonic (compressional, shear and stoneley slownesses), and caliper logs. After that, the fracture index permeability was estimated by improving the FZI permeability equation. The coherence coefficient between two estimated fracture permeability logs is 0.66. A good correlation is observed on the high permeability zones, but the lower correlation on the low permeability zones. It is notified that, in the high fracture permeability zones, the conventional logs are heavily impacted by fracture permeability. However, due to lower vertical resolution of conventional logs compared with the image logs, the conventional logs are less influenced by less dense fracture zones. However, this algorithm can be used with acceptable accuracy in all uncored and image log wells.

Estimation of the horizontal in-situ stress magnitude and azimuth using previous drilling data

Oil exploration and production,well stability,sand production,geothermal drilling,waste-water or CO_(2) sequestration,geohazards assessment,and EOR processes such as hydraulic fracturing,require adequate information about in-situ stresses.There are several methods for analyzing the magnitude and direction of in-situ stresses.The evaluation of tensile fractures and shear fractures in vertical oil and gas wellbores using image logs is one of these methods.Furthermore,when image logs are run in boreholes,they can be extremely costly and possibly stop the drilling.The data for this study were gathered from seven directional wells drilled into a strike-slip fault reservoir in southern Iran.Vertical stress,minimum horizontal stress,pore pressure,Poisson's ratio of formations,and 233 mud loss reporting points make up the entire data.This is the first time maximum horizontal stress direction has been calculated without referring to image log data.In addition,the points of lost circulation were categorized into natural and induced fracture.The results revealed that,the maximum horizontal stress direction of the reservoir was calculated at 65
northeast-southwest.The error rate is roughly 10
when comparing the results of this investigation to those obtained from the image log.The maximum horizontal stress direction is calculated precisely.In terms of tensile fracture pressure,the in-situ stress ratio identifies the safest as well as the most critical inclination and azimuth for each well.