Fractal Correction of Well Logging Curves

It is always significant for assessing and evaluation of oil bearing layers, especially for well logging data processing and interpretation of non marine oil beds to get more accurate physical properties in thin and inter thin layers. This paper presents a definition of measures and the measure presents power law relation with the corresponded scale described by fractal theory. Thus, logging curves can be reconstructed according to this power law relation. This method uses the local structure nearby concurrent points to compensate the average effect of logging probes and measurement errors. As an example, deep and medium induced conductivity (IMPH and IDPH) curves in ODP Leg 127 Hole 797C are reconstructed or corrected. Corrected curves are with less adjacent effects through comparison of corrected curves with original one. And also, the power spectra of corrected well logging curve are abounding with more resolution components than the original one. Thus, fractal correction method makes the well logging more resoluble for thin beds.

Acousto-electric well logging by eccentric source and extraction of shear wave

The nonaxisymmetric acousto-electric field excited by an eccentric acoustic source in the borehole based on Pride seismoelectric theory is considered. It is shown that the acoustic field inside the borehole, converted electric and magnetic fields and coupled fields outside the borehole are composed of an infinitude of multipole fields with different orders. The numerical results show that both the electromagnetic waves and the seismoelectric field in the borehole, and the three components of both electric field and magnetic field can be detected. Measurements on the borehole axis will be of advantage to determining shear velocity information. The components of the symmetric and nonsymmetric acoustic and electromagnetic fields can be strengthened or weakened by adding or subtracting the two full waveforms logged in some azimuths. It may be a new method of directly measuring the shear wave velocity by using the borehole seismoelectric effect.

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.

Identification and Evaluation of Low Resistivity Pay Zones by Well Logs and the Petrophysical Research in China

This paper presents an overview of petrophysical research and exploration achievements of low resistivity pay （LRP） zone by well logs in China. It includes geological characteristics and characteristics of well log response of the low resistivity pay zones discovered and evaluated in recent years, as well as the problems in recognizing and evaluating low resistivity pay zones by well logs. The research areas mainly include the Neogene formations in the Bohai Bay Basin, the Triassic formations in the northern Tarim Basin and the Cretaceous formations in the Junggar Basin, The petrophysical research concerning recognition and evaluation of the low resistivity pays, based on their genetic types, is introduced in this paper.

Simulation Experiments in Electromagnetic Tomography Well Logging in Two-phase Flow

Electromagnetic Computer Tomography （ECT） is a method to probe the interior of an inhomogeneous medium via surface measurement in a non-linear way. Due to the great differences in conductivity and permittivity between oil and water in the well, Electromagnetic Tomography Well Logging （ETWL）, a new flow imaging measurement system, is proposed to describe the distribution and movement of oil/water two-phase flow in the well by scanning the detected region and applying a suitable data processing algorithm. The results of the numerical simulation and physical modeling show that the system could provide a clear image of the flow profile.

Application of a Method for Calculating the Organic Carbon Content by Well Logs to Faulted Basins

The source rock model used in this project was developed by French Petroleum Research Institute. The total organic carbon content was estimated primarily and directly by using continuous conventional logging curves (such as sonic and resistivity curves), which are calibrated through the laboratory analysis data of organic carbon of cores, cuttings or sidewall cores. Regional evaluations have been carried out in downwarping basins abroad. The Haila′er Basin is a faulted basin and the evaluation of such a basin is a new subject. On the basis of a regional evaluation method for the downwarping basins, a new method suitable to faulted basins was developed. The effect is satisfactory when this new method is applied to the Wu′erxun Sag and the Bei′er Sag.

Study on Calculation Method of Compressional Velocities Based on Field Well Logs

In the past, most of the studies for compressional velocities are based on experimental measurements, which lack the support of field data. The purpose of this study is to estimate the compressional velocities based on well log data of delta front subfacies of Lower Tertiary ages of Ji-Dong oil field, China. At initial stage, we have chosen the well log parameters (effect factors) which strongly influence on compressional velocities and established a new modified equation for compressional velocities, which is based on these effect factors. Then Gardner, De-hua Han and this newly established equation were utilized to calculate the compressional velocities in each well. Finally, Least-square regression was carried out to check the fitting of each equation. Regression results clearly indicate that our purposed equation shows better fitting as compared to Gardner and De-hua Han equations.

Total Organic Carbon Enrichment and Source Rock Evaluation of the Lower Miocene Rocks Based on Well Logs: October Oil Field, Gulf of Suez-Egypt

October oil field is one of the largest hydrocarbon-bearing fields which produces oil from the sand section of the Lower Miocene Asl Formation. Two marl (Asl Marl) and shale (Hawara Formation) sections of possible source enrichment are detected above and below this oil sand section, respectively. This study aims to identify the content of the total organic carbon based on the density log and a combination technique of the resistivity and porosity logs (Δlog R Technique). The available geochemical analyses are used to calibrate the constants of the TOC and the level of maturity (LOM) used in the (Δlog R Technique). The geochemical-based LOM is found as 9.0 and the calibrated constants of the Asl Marl and Hawara Formation are found as 11.68, 3.88 and 8.77, 2.80, respectively. Fair to good TOC% content values (0.88 to 1.85) were recorded for Asl Marl section in the majority of the studied wells, while less than 0.5% is recorded for the Hawara Formation. The lateral distribution maps show that most of the TOC% enrichments are concentrated at central and eastern parts of the study area, providing a good source for the hydrocarbons encountered in the underlying Asl Sand section.

Synthetic well logs generation via Recurrent Neural Networks

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.

Integration of Well Logs and Seismic Data for Prospects Evaluation of an X Field, Onshore Niger Delta, Nigeria

Hydrocarbon reservoir beds have been delineated using direct hydrocarbon indicator on seismic sections as well as well logs data in X field, Onshore Niger Delta. The research methodology involved horizon interpretation to produce sub-surface structure map. Geophysical well log signatures were employed in identifying hydrocarbon bearing sand. The well-to-seismic tie revealed that the reservoir tied directly with hydrocarbon indicator (bright spot) on the seismic sections. The major structure responsible for the hydrocarbon entrapment is anticline. The crest of the anticline from the depth structural map occurs at 3450 metres.

Application of Activity and Wavelet Analysis on Well Logging Sedimentary Cycle Division

The method of sedimentary cycle division is studied based on comprehensive research and activity analysis of well logging, combined with wavelet analysis and characteristics of stratigraphic cycle. Based on the method above, this paper divided stratigraphic cycles and finely classified the tratifigraphic by taking H1518 in HSS oilfield as an example. The result shows that sedimentary cycle can be divided effectively based on key stratum study, activity and wavelet analysis of well log, and the research of sedimentary cycle characteristics. H1 formation can be divided into 1 sand group, 3 sand layers and 7 single layers.

Sedimentology, Structure and Depositional Environments of the Subsurface Cretaceous, North Sinai, Egypt Using Well Logs

A complete set of well logs was used to study the sedimentology, structural and depositional environments of the subsurface Cretaceous rocks of northern Sinai, as a promising hydrocarbon province in Egypt. The sedimentological interpretations of well logs show sedimentary sequence of the Early Cretaceous, represented by the Neocomian, Aptian and Albian, which is composed mainly of shales and marls with minor carbonate and sandstone intercalations. Based on the Th/K ratios, the argillaceous sediments are composed of illite, montmorillonite and micas with little amounts of glauconite and chlorite. The environmental interpretations revealed sedimentological environments vary from estuarine to lagoonal and backreef of the Barremian-Aptian rocks, and from continental to estuarine in the Aptian-Albian. By contrast, the Late Cretaceous rocks, represented by the Cenomanian, Turonian, Santonian and Maastrichtian, are mainly composed of carbonates with few shale and marl intercalations. This sequence was accumulated under lagoonal to neritic and bathyal environments. The structural interpretations of well logs show that the Cretaceous section attains a wide range of dip magnitudes and dispersed azimuths all over the study area, which is probably attributed to tectonic and sedimentological processes. The inherited structural complexities indicate possible rejuvenations along old fault planes and rotation of the faulted blocks.

Well Logging and Perforation Technology in China

Well logging, Perforation, Data analysis, Interpretation

Well Logging Responses of UHP Metamorphic Rocks from CCSD Main Hole in Sulu Terrane,Eastern Central China

The lithologies of the Chinese Continental Scientific Drilling main hole （CCSD-MH） are mainly comprised of orthogneiss,paragneiss,eclogite,amphibolite,and ultramafic rocks.The statistical results of logs of CCSD-MH indicate that ultramafic rocks are characterized by very high CNL （neutron log） and very low GR （gamma ray log） and RD （resistivity log）;eclogites are characterized by high DEN （density）,VP （P-wave velocity） and PE （photoelectric absorption capture cross section）;orthogneiss and paragneiss are characterized by high GR,U （uranium content）,Th （thorium content）,K （potassium content） and RD,and low DEN,PE,and CNL;logging values of amphibolite are between the logging values of eclogites and paragneiss.In addition,the logs could reflect the degree of retrograde metamorphism of eclogites.The upper section （100-2 000 m） shows higher DEN,PE,VP,and lower GR,U,Th,K,RD than the lower section （2 000-5 000 m）.Most logs of the upper section are more fluctuant than those of the lower section.This indicates that the upper section has more heterogeneities than the lower section.The cross plots of logs indicate that DEN,GR,K,and CNL are more powerful in identifying ultrahigh pressure metamorphic （UHPM） rocks at the CCSD-MH.GR value of the rocks from CCSD-MH shows obviously an increasing trend from ultramafic rock （the most mafic rocks at CCSD-MH） to orthogneiss （the most acid rocks at CCSD-MH）.On the contrary,DEN value decreases from the ultramafic rock to the orthogneiss.CNL log is a good indicator of the content of structure water in crystalline rocks.

Modeling of Borehole Radar for Well Logging Using Pseudo-spectral Time Domain Algorithm

In this article, numerical modeling of borehole radar for well logging in time domain is developed using pseudo-spectral time domain algorithm in axisymmetric cylindrical coordinate for proximate true formation model. The conductivity and relative permittivity logging curves are obtained from the data of borehole radar for well logging. Since the relative permittivity logging curve is not affected by salinity of formation water, borehole radar for well logging has obvious advantages as compared with conventional electrical logging. The borehole radar for well logging is a one-transmitter and two-receiver logging tool. The conductivity and relative permittivity logging curves are obtained successfully by measuring the amplitude radio and the time difference of pulse waveform from two receivers. The calculated conductivity and relative permittivity logging curves are close to the true value of surrounding formation, which tests the usability and reliability of borehole radar for well logging. The numerical modeling of borehole radar for well logging laid the important foundation for researching its logging tool.

On the prediction of geochemical parameters(TOC,S1 and S2)by considering well log parameters using ANFIS and LSSVM strategies

Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty.Though,attaining these parameters,for instance total organic carbon(TOC),volatile and residual hydrocarbon(S1&S2)is a challenge for geologists due to the high cost and time consumption.Therefore,addressing this issue has become an interesting subject for many researchers.As a result,on the ground of conventional well logs,vast kinds of methods,for example,back propagation artificial neural network(BPANN),have been introduced to solve this problem.Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime.However,the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum.Therefore,current study cope with this issue and alleviate the uncertainty,Least Squares Support Vector Machine(LSSVM)and Adaptive-Neuro Fuzzy Inference System(ANFIS)algorithms cooperating with the particle swarm optimization(PSO)were suggested as a suitable method to increase the precision of estimating geochemical factors.The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China,which consists of geochemical and well logging information.Outputs of this study illustrated that ANFIS-PSO and LSSVMPSO have a great ability to estimate geochemical parameters.The values of R^(2) obtained for these two models in order to predict the output parameters of TOC,S_(1) and S_(2) are equal to 0.6846&0.785,0.6864&0.778,and 0.7343&0.8128,respectively.The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model.Also,a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters.