To effectively replace the isotope radiation source in litho-density logging,this study presents a method for measuring the formation density and photoelectric absorption index(Pe)using a switchable X-ray tube.First,t...To effectively replace the isotope radiation source in litho-density logging,this study presents a method for measuring the formation density and photoelectric absorption index(Pe)using a switchable X-ray tube.First,the gamma-ray litho-density logging(GLD)method for measuring formation density and Pe using chemical sources is introduced.Then,a benchmark verification based on the X-ray litho-density logging tool prototype and data published by Simon(In:Paper presented at the SPWLA 59th annual logging symposium,London,UK,2018)was carried out using Monte Carlo numerical simulations.Second,the impacts of the photoelectric effect and detector statistical error on the GLD method were analyzed.Finally,based on a theoretical analysis,the formation density and Pe measurement algorithm(double energy window(DEW)method)was improved,which was found to be suitable for X-ray litho-density logging.Moreover,the results obtained using this algorithm were compared with those obtained using the GLD method.The results indicate that owing to the impact of photoelectric effect and detector statistical error on the density energy window,the accuracy of formation density and Pe measurement using the GLD method is relatively low,with the uncertainty in formation density and Pe measurement reaching 2.620±0.047 g/cm3 and 4.090±0.580 b/e,respectively.In comparison,the DEW method can improve the accuracy of density and Pe measurement to 0.006 g/cm3 and 0.065 b/e,respectively,as the photoelectric effect in the density window is corrected using the counts in the lithology window of the energy spectrum.This study aims to provide a new theoretical foundation for processing X-ray litho-density logs in the future.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit wh...Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.展开更多
In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and...In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and wellbore stability.To address this,it is necessary to conduct geology-engineering integration studies.Based on the formation resistivity,an analytical model was developed to assess the impact of KCl concentration in the WBDF on array induction logging response accuracy.The maximum permissible KCl concentration for the target formations was determined,and technical strategies were proposed to maintain wellbore stability at a reduced KCl concentration.After that,considering the inhibitory,encapsulating,and plugging effects,a low-KCl-concentration WBDF was optimized and applied.Model calculations demonstrate that increasing KCl concentration in the WBDF decreases resistivity,thereby reducing logging accuracy.To maintain a logging accu racy of ≥80%,the upper limits for KCl concentration in the WBDF are 4.8%,4.2%,and 3.6% for the 3rd Member of the Dongying Formation,the 1st and 2nd members of the Shahejie Formation,respectively.Cuttings recovery experiments revealed that a minimum KCl concentration of 3% is required to ensure basic shale inhibition.A combination of 3% KCl with 1% polyamine inhibitor yielded cuttings recovery and shale stability index comparable to those achieved with 7% KCl alone,and the shale inhibition performance was further enhanced with the addition of an encapsulator.The optimized WBDF has been successfully deployed in exploration/appraisal wells across multiple blocks within Dagang Oilfield,resulting in superior wellbore stability during ope rations.Furthermore,the electric logging interpre tation coincidence rate improved from 68.1% to 89.9%,providing robust te chnical support for high-quality drilling and accurate reservoir evaluation in exploration/appraisal wells.展开更多
The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,includin...The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,including rock brittleness,wellbore stability,fracturing effects,and invasion extent,thereby enhancing comprehension of formation structures and informing exploration strategies.However,traditional near-wellbore formation velocity imaging methods exhibit two principal limitations.First,these methods lack azimuthal sensitivity,yielding results averaged across all directions.Second,they are computationally intensive and impractical for well-site environments.To address these drawbacks,we developed a rapid 3D velocity imaging method for array acoustic logging instru ments equipped with azimuthal receivers,capable of producing 3D imaging results efficiently.The workflow entails the following steps:(1)Band-pass filtering of logging data to mitigate scattered wave interference caused by formation heterogeneity near the wellbore;(2)combination of receivers with varying detection ranges in each direction to derive radial velocity sequences,followed by integration of ray-tracing theory to obtain 2D velocity distributions;and(3)synthesis of final 3D velocity imaging results via interpolation of these 2D datasets.In the velocity sequence extraction process,we significantly reduced the computational load by employing an adaptive time window,ensuring rapid and stable application in well-site settings.We utilized the finite difference method to construct well models with heteroge neous formations.The compressional and shear wave 3D velocity imaging results derived from synthetic data correlated with the model,demonstrating the azimuthal sensitivity of our proposed method.Furthermore,we applied this method to a well in West China,successfully identifying the azimuth of nearwellbore anisotropy.展开更多
A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and trans...A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.展开更多
Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the...Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.展开更多
China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resou...China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.展开更多
To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the p...To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.展开更多
Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes ...Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes an intelligent method for detecting fractures and holes,as well as segmenting whole-wellbore images.Firstly,we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images.A standardized process procedure for the generation of new samples and model training has been proposed effectively.Subsequently,the improved YOLOv8 model undergoes a process of training and validation.The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks,respectively.These findings demonstrate a notable performance improvement compared to the original model.Furthermore,a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images.To manage overlapping regions within the sliding window,we employ the Non-Maximum Suppression(NMS)principle for effective processing.Finally,the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes,which significantly improves the efficiency of geological feature recognition in the wholewell section ultrasonic logging images.展开更多
The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly prono...The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.展开更多
The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration an...The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration and development and the requirements of low-carbon development,the focus of exploration anddevelopment in the oil and gas industry is gradually shifting to the exploration and development of renewableenergy sources such as deep sea,deep earth and geothermal energy.The traditional petrophysical evaluation andinterpretation model has encountered great challenges in the face of new evaluation objects.To establish a distributedlogging big data private cloud platform with a unified learning model as the key,which realizes the distributed storageand processing of logging big data,and enables the learning of brand-new knowledge patterns from multi-attributedata in the large function space in the unified logging learning model integrating the expert knowledge and the datamodel,so as to solve the problem of geoengineering evaluation of geothermal fields.Based on the research ideaof“logging big data cloud platform---unified logging learning model---large function space---knowledge learning&discovery---application”,the theoretical foundation of unified learning model,cloud platform architecture,datastorage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storageand processing of data and learning algorithms.New knowledge of geothermal evaluation is found in a large functionspace and applied to Geo-engineering evaluation of geothermal fields.The examples show its good application in theselection of logging series in geothermal fields,quality control of logging data,identification of complex lithologyin geothermal fields,evaluation of reservoir fluids,checking of associated helium,evaluation of cementing quality,evaluation of well-side fractures,and evaluation of geothermal water recharge under the remote logging module ofthe cloud platform.The first and second cementing surfaces of cemented wells in geothermal fields were evaluated,as well as the development of well-side distal fractures,fracture extension orientation.According to the well-sidefracture communication to form a good fluid pathway and large flow rate and long flow diameter of the thermalstorage fi ssure system,the design is conducive to the design of the recharge program of geothermal water.展开更多
Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within wate...Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.展开更多
It is still argued whether we measure phase or group velocities using acoustic logging tools. In this paper, three kinds of models are used to investigate this problem by theoretical analyses and numerical simulations...It is still argued whether we measure phase or group velocities using acoustic logging tools. In this paper, three kinds of models are used to investigate this problem by theoretical analyses and numerical simulations. First, we use the plane-wave superposition model containing two plane waves with different velocities and able to change the values of phase velocity and group velocity. The numerical results show that whether phase velocity is higher or lower than group velocity, using the slowness-time coherence （STC） method we can only get phase velocities. Second, according to the results of the dispersion analysis and branch-cut integration, in a rigid boundary borehole model the results of dispersion curves and the waveforms of the first-order mode show that the velocities obtained by the STC method are phase velocities while group velocities obtained by arrival time picking. Finally, dipole logging in a slow formation model is investigated using dispersion analysis and real-axis integration. The results of dispersion curves and full wave trains show similar conclusions as the borehole model with rigid boundary conditions.展开更多
The reservoir pore structure controls the reservoir quality and resistivity response of hydrocarbon-bearing zones and thus, critically affects logging interpretation. We use petrophysical data in three types of reserv...The reservoir pore structure controls the reservoir quality and resistivity response of hydrocarbon-bearing zones and thus, critically affects logging interpretation. We use petrophysical data in three types of reservoir with different pore structure characteristics to show that the complexity of pore structure had a significant effect on the effective porosity and permeability regardless of geological factors responsible for the formation of pore structure. Moreover,, the distribution and content of conductive fluids in the reservoir varies dramatically owing to pore structure differences, which also induces resistivity variations in reservoir rocks. Hence, the origin of low-resistivity hydrocarbon-bearing zones, except for those with conductive matrix and mud filtrate invasion, is attributed to the complexity of the pore structures. Consequently, reservoir-specific evaluation models, parameters, and criteria should be chosen for resistivity log interpretation to make a reliable evaluation of reservoir quality and fluids.展开更多
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...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.展开更多
We designed a new downhole electrokinetic logging tool based on numericalsimulations and petrophysical experiments. Acoustic and electric receivers cannot be arrangedat the same depth, and the proposed composite elect...We designed a new downhole electrokinetic logging tool based on numericalsimulations and petrophysical experiments. Acoustic and electric receivers cannot be arrangedat the same depth, and the proposed composite electrokinetic logging tool offers a solutionto this problem. The sound field characteristics of the detectors were tested in a water tank inthe laboratory. Then, we calculated the sound pressure of the radiated acoustic field and thetransmitting voltage response of the transmitting transducers; in addition, we analyzed thedirectivity and application of the acoustic transmitting probe based on linear phased array.The results suggest that the sound pressure generated at 1500 mm spacing reaches up to 47.2k Pa and decreases with increasing acoustic source frequency. When the excitation signalsdelay time of adjacent acoustic transmitting subarrays increases, the radiation beam of themain lobe is deflected and its energy gradually increases, which presumably enhances theacoustoelectric conversion efficiency.展开更多
The results of a heat-conduction experiment with a central point source in a sand barrel shows that the temperature of the heat source increase much faster in sand saturated with oil and air （dry sand） than in water...The results of a heat-conduction experiment with a central point source in a sand barrel shows that the temperature of the heat source increase much faster in sand saturated with oil and air （dry sand） than in water sand. During cooling the temperature of the central heat source goes down slower in oil- or air-saturated sands than in water sands. Based on the theory of heat-conduction in porous media and the experimental results, we developed a new heat-conduction logging technique which utilizes an artificial heat source （dynamite charge or electric heater） to heat up target forma- tions in the borehole and then measure the change of temperature at a later time. Post-frac oil production is shown to be directly proportional to the size of the temperature anomaly when other reservoir parameters are fairly consistent. The method is used to evaluate potential oil production for marginal reservoirs in the FY formation in Song-Liao basin of China.展开更多
Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accur...Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accurate calculation of mineral compositions. Previousprocessing methods calculated the formation composition via the conversion relation betweenthe formation chemistry and minerals. Thus, their applicability is limited and the methodprecision is relatively low. In this study, we present a multimineral optimization processingmethod based on the ECS log. We derived the ECS response equations for calculating theformation composition, then, determined the logging response values for the elements ofcommon minerals using core data and theoretical calculations. Finally, a software modulewas developed. The results of the new method are consistent with core data and the meanabsolute error is less than 10%.展开更多
基金the National Natural Science Foundation of China(Nos.41674129,41874147)National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2017ZX05009-001)+1 种基金Fundamental Research Funds for Central Universities(No.19CX02004A)National Key Research and Development Program of China(No.2016YFC0302800).
文摘To effectively replace the isotope radiation source in litho-density logging,this study presents a method for measuring the formation density and photoelectric absorption index(Pe)using a switchable X-ray tube.First,the gamma-ray litho-density logging(GLD)method for measuring formation density and Pe using chemical sources is introduced.Then,a benchmark verification based on the X-ray litho-density logging tool prototype and data published by Simon(In:Paper presented at the SPWLA 59th annual logging symposium,London,UK,2018)was carried out using Monte Carlo numerical simulations.Second,the impacts of the photoelectric effect and detector statistical error on the GLD method were analyzed.Finally,based on a theoretical analysis,the formation density and Pe measurement algorithm(double energy window(DEW)method)was improved,which was found to be suitable for X-ray litho-density logging.Moreover,the results obtained using this algorithm were compared with those obtained using the GLD method.The results indicate that owing to the impact of photoelectric effect and detector statistical error on the density energy window,the accuracy of formation density and Pe measurement using the GLD method is relatively low,with the uncertainty in formation density and Pe measurement reaching 2.620±0.047 g/cm3 and 4.090±0.580 b/e,respectively.In comparison,the DEW method can improve the accuracy of density and Pe measurement to 0.006 g/cm3 and 0.065 b/e,respectively,as the photoelectric effect in the density window is corrected using the counts in the lithology window of the energy spectrum.This study aims to provide a new theoretical foundation for processing X-ray litho-density logs in the future.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金co-funded by the National Key Research and Development Program of China under Grant (2019YFA0708301)the CAS Project for Young Scientists in Basic Research (Grant No.YSBR-082)Research Instrument and Equipment Development Project of Chinese Academy of Sciences (GJJSTD20210008)。
文摘Electromagnetic technology used in logging while drilling(LWD) provides the resistivity distribution around a borehole within a range of several tens of meters.However,a blind zone appears in front of the drill bit when operating in high-angle wells,limiting the ability to detect formations ahead of the drill bit.Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling.In this study,we examine the detection capabilities of various component combinations of magnetic dipole antenna.Based on the sensitivity of each component to the axial information,a coaxial component is selected as a boundary indicator.We investigate the impact of various factors,such as frequency and transmitter and receiver(TR) distance,under different geological models.This study proposes 5 and 20 kHz as appropriate frequencies,and 10-14 and 12-17 m as suitable TR distance combinations.The accuracy of the numerical calculation results is verified via air-sea testing,confirming the instrument's detection capability.A test model that eliminated the influence of environmental factors and seawater depth is developed.The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead.It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.
基金supported by the National Natural Science Foundation of China (52474024)the National Oil&Gas Major Project of China (2025ZD1403200)the Program for Scientific Research Innovation Team of Young Scholars in Colleges and Universities of Shandong Province"Innovation Team of Deepwater Wellbore Fluids"(2022KJ069)。
文摘In Dagang Oilfield in China,the utilization of the KCl polymer water-based drilling fluid(WBDF) in middeep exploration/appraisal wells presents a challenge in simultaneously optimizing resistivity logging accuracy and wellbore stability.To address this,it is necessary to conduct geology-engineering integration studies.Based on the formation resistivity,an analytical model was developed to assess the impact of KCl concentration in the WBDF on array induction logging response accuracy.The maximum permissible KCl concentration for the target formations was determined,and technical strategies were proposed to maintain wellbore stability at a reduced KCl concentration.After that,considering the inhibitory,encapsulating,and plugging effects,a low-KCl-concentration WBDF was optimized and applied.Model calculations demonstrate that increasing KCl concentration in the WBDF decreases resistivity,thereby reducing logging accuracy.To maintain a logging accu racy of ≥80%,the upper limits for KCl concentration in the WBDF are 4.8%,4.2%,and 3.6% for the 3rd Member of the Dongying Formation,the 1st and 2nd members of the Shahejie Formation,respectively.Cuttings recovery experiments revealed that a minimum KCl concentration of 3% is required to ensure basic shale inhibition.A combination of 3% KCl with 1% polyamine inhibitor yielded cuttings recovery and shale stability index comparable to those achieved with 7% KCl alone,and the shale inhibition performance was further enhanced with the addition of an encapsulator.The optimized WBDF has been successfully deployed in exploration/appraisal wells across multiple blocks within Dagang Oilfield,resulting in superior wellbore stability during ope rations.Furthermore,the electric logging interpre tation coincidence rate improved from 68.1% to 89.9%,providing robust te chnical support for high-quality drilling and accurate reservoir evaluation in exploration/appraisal wells.
基金supported by the National Natural Science Foundation of China(Grant Nos.42174129 and 41374143)the project of"Vice President of Science and Technology"of Changping District,Beijing(Grant No.202502003019)。
文摘The characterization of subsurface formations via the analysis of near-wellbore velocity profiles represents a crucial method in geophysical exploration.This technique enables the evaluation of key parameters,including rock brittleness,wellbore stability,fracturing effects,and invasion extent,thereby enhancing comprehension of formation structures and informing exploration strategies.However,traditional near-wellbore formation velocity imaging methods exhibit two principal limitations.First,these methods lack azimuthal sensitivity,yielding results averaged across all directions.Second,they are computationally intensive and impractical for well-site environments.To address these drawbacks,we developed a rapid 3D velocity imaging method for array acoustic logging instru ments equipped with azimuthal receivers,capable of producing 3D imaging results efficiently.The workflow entails the following steps:(1)Band-pass filtering of logging data to mitigate scattered wave interference caused by formation heterogeneity near the wellbore;(2)combination of receivers with varying detection ranges in each direction to derive radial velocity sequences,followed by integration of ray-tracing theory to obtain 2D velocity distributions;and(3)synthesis of final 3D velocity imaging results via interpolation of these 2D datasets.In the velocity sequence extraction process,we significantly reduced the computational load by employing an adaptive time window,ensuring rapid and stable application in well-site settings.We utilized the finite difference method to construct well models with heteroge neous formations.The compressional and shear wave 3D velocity imaging results derived from synthetic data correlated with the model,demonstrating the azimuthal sensitivity of our proposed method.Furthermore,we applied this method to a well in West China,successfully identifying the azimuth of nearwellbore anisotropy.
基金supported by the National Natural Science Foundation of China (grant nos. 42074137)。
文摘A cased well model consists of a coaxial tank and casing,which houses coaxially installed transmitting and receiving coils.The transmitting coil is excited by the current produced by the transmitting circuit,and transient electromagnetic responses occur in the casing,including direct coupling and casing responses.As the range between the transmitting and receiving coils increases,direct coupling responses decay rapidly,are less than the casing response at 0.3 m,and disappear at 0.7 m.By contrast,a casing response increases rapidly and then declines slowly after reaching a peak and changes little within a specifi c range.The peak decreases slowly with range.The continuous addition of water to the tank causes slight changes in transient electromagnetic responses,so the diff erence which are subtracted from the response without water is used.Moreover,the diff erences at the time of rapid increase in response and the time of rapid decrease in response are large,forming a peak and a trough.Given that the conductivity of water in a full tank changes after the addition of salt,the diff erence in the peak is linear with the increase in the conductivity of water.This result provides an experimental basis for the design of a transient electromagnetic logging instrument that measures the conductivity of formation in cased well.
基金the support of the National Natural Science Foundation of China(42174147,42474155)the Scientific and Technological Innovation Projects of Laoshan Laboratory(LSKJ20220347)。
文摘Neutron well logging,using instruments equipped with neutron source and detectors(e.g.,^(3)He-tubes,Nal,BGO),plays a key role in lithological differentiation,porosity determination,and fluid property evaluation in the petroleum industry.The growing trend of multifu nctional neutron well logging,which enables simultaneous extraction of multiple reservoir characteristics,requiring high-performance detectors capable of withstanding high-temperature downhole conditions,limited space,and instrument vibrations,while also detecting multiple particle types.The Cs_(2)LiYCl_(6):Ce^(3+)(CLYC)elpasolite scintillator demonstrates excellent temperature resistance and detection efficiency,making it become a promising candidate for leading the development of the novel neutron-based double-particle logging technology.This study employed Monte Carlo simulations to generate equivalent gamma spectra and proposed a pulse shape discrimination simulation method based on theoretical analysis and probabilistic iteration.The performance of CLYC was compared to that of common detectors in terms of physical properties and detection efficiency.A double-particle pulsed neutron detection system for porosity determination was established,based on the count ratio of equivalent gamma rays from the range of 2.95-3.42 MeVee energy bins.Results showed that CLYC can effectively replace ^(3)He-tubes for porosity measurement,providing consistent responses.This study offers numerical simulation support for the design of future neutron well logging tools and the application of double-particle detectors in logging systems.
文摘China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.
基金Supported by the National Natural Science Foundation of China(52288101)National Key R&D Program of China(2024YFF1500600)。
文摘To improve the accuracy and generalization of well logging curve reconstruction,this paper proposes an artificial intelligence large language model“Gaia”and conducts model evaluation experiments.By fine-tuning the pre-trained large language model,the Gaia significantly improved its ability in extracting sequential patterns and spatial features from well-log curves.Leveraging the adapter method for fine-tuning,this model required training only about 1/70 of its original parameters,greatly improving training efficiency.Comparative experiments,ablation experiments,and generalization experiments were designed and conducted using well-log data from 250 wells.In the comparative experiment,the Gaia model was benchmarked against cutting-edge small deep learning models and conventional large language models,demonstrating that the Gaia model reduced the mean absolute error(MAE)by at least 20%.In the ablation experiments,the synergistic effect of the Gaia model's multiple components was validated,with its MAE being at least 30%lower than that of single-component models.In the generalization experiments,the superior performance of the Gaia model in blind-well predictions was further confirmed.Compared to traditional models,the Gaia model is significantly superior in accuracy and generalization for logging curve reconstruction,fully showcasing the potential of large language models in the field of well-logging.This provides a new approach for future intelligent logging data processing.
基金supported by the National Natural Science Foundation of China(Grant Nos.12334019,12304496).
文摘Aiming to address the demand for intelligent recognition of geological features in whole-wellbore ultrasonic images,this paper integrates the YOLOv8 model with the Convolution Block Attention Module(CBAM).It proposes an intelligent method for detecting fractures and holes,as well as segmenting whole-wellbore images.Firstly,we develop a dataset sample of effective reservoir sections by integrating logging data and conducting data augmentation on fracture and hole samples in ultrasonic logging images.A standardized process procedure for the generation of new samples and model training has been proposed effectively.Subsequently,the improved YOLOv8 model undergoes a process of training and validation.The results indicate that the model achieves average accuracies of 0.910 and 0.884 in target detection and image segmentation tasks,respectively.These findings demonstrate a notable performance improvement compared to the original model.Furthermore,a sliding window strategy is proposed to tackle the challenges of high computational demands and insufficient accuracy in the intelligent processing of full-well ultrasonic images.To manage overlapping regions within the sliding window,we employ the Non-Maximum Suppression(NMS)principle for effective processing.Finally,the model has been tested on actual logging images and demonstrates an enhanced capability to identify irregular fractures and holes,which significantly improves the efficiency of geological feature recognition in the wholewell section ultrasonic logging images.
基金supported by Scientific Research and Technology Development Project of CNPC(2021DJ4002,2022DJ3908).
文摘The numerical dispersion phenomenon in the finite-difference forward modeling simulations of the wave equation significantly affects the imaging accuracy in acoustic reflection logging.This issue is particularly pronounced in the reverse time migration(RTM)method used for shear-wave(S-wave)logging imaging.This not only affects imaging accuracy but also introduces ambiguities in the interpretation of logging results.To address this challenge,this study proposes the use of a least-squares difference coefficient optimization algorithm aiming to suppress the numerical dispersion phenomenon in the RTM of S-wave reflection imaging logging.By optimizing the difference coefficients,the high-precision finite-difference algorithm serves as an effective operator for both forward and backward RTM processes.This approach is instrumental in eliminating migration illusions,which are often caused by numerical dispersion.The effectiveness of this optimized algorithm is demonstrated through numerical results,which indicate that it can achieve more accurate forward imaging results across various conditions,including high-and low-velocity strata,and is effective in both large and small spatial grids.The results of processing real data demonstrate that numerical dispersion optimization effectively reduces migration artifacts and diminishes ambiguities in logging interpretations.This optimization offers crucial technical support to the RTM method,enhancing its capability for accurately modeling and imaging S-wave reflections.
文摘The development of machine learning and deep learning algorithms as well as the improvement ofhardware arithmetic power provide a rare opportunity for logging big data private cloud.With the deepeningof exploration and development and the requirements of low-carbon development,the focus of exploration anddevelopment in the oil and gas industry is gradually shifting to the exploration and development of renewableenergy sources such as deep sea,deep earth and geothermal energy.The traditional petrophysical evaluation andinterpretation model has encountered great challenges in the face of new evaluation objects.To establish a distributedlogging big data private cloud platform with a unified learning model as the key,which realizes the distributed storageand processing of logging big data,and enables the learning of brand-new knowledge patterns from multi-attributedata in the large function space in the unified logging learning model integrating the expert knowledge and the datamodel,so as to solve the problem of geoengineering evaluation of geothermal fields.Based on the research ideaof“logging big data cloud platform---unified logging learning model---large function space---knowledge learning&discovery---application”,the theoretical foundation of unified learning model,cloud platform architecture,datastorage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storageand processing of data and learning algorithms.New knowledge of geothermal evaluation is found in a large functionspace and applied to Geo-engineering evaluation of geothermal fields.The examples show its good application in theselection of logging series in geothermal fields,quality control of logging data,identification of complex lithologyin geothermal fields,evaluation of reservoir fluids,checking of associated helium,evaluation of cementing quality,evaluation of well-side fractures,and evaluation of geothermal water recharge under the remote logging module ofthe cloud platform.The first and second cementing surfaces of cemented wells in geothermal fields were evaluated,as well as the development of well-side distal fractures,fracture extension orientation.According to the well-sidefracture communication to form a good fluid pathway and large flow rate and long flow diameter of the thermalstorage fi ssure system,the design is conducive to the design of the recharge program of geothermal water.
基金supported by the National Natural Science Foundation of China(No.31660234).
文摘Global forest cover is undergoing significant transformations due to anthropogenic activities and natural disturbances,profoundly impacting hydrological processes.However,the inherent spatial heterogeneity within watersheds leads to varied hydrological responses across spatiotemporal scales,challenging comprehensive assessment of logging impacts at the watershed scale.Here,we developed multiple forest logging scenarios using the soil and water assessment tool(SWAT)model for the Le'an River watershed,a 5,837 km2 subtropical watershed in China,to quantify the hydrological effects of forest logging across different spatiotemporal scales.Our results demonstrate that increasing forest logging ratios from 1.54% to 9.25% consistently enhanced ecohydrological sensitivity.However,sensitivity varied across spatiotemporal scales,with the rainy season(15.30%-15.81%)showing higher sensitivity than annual(11.56%-12.07%)and dry season(3.38%-5.57%)periods.Additionally,the ecohydrological sensitivity of logging varied significantly across the watershed,with midstream areas exhibiting the highest sensitivity(13.13%-13.25%),followed by downstream(11.87%-11.98%)and upstream regions(9.96%-10.05%).Furthermore,the whole watershed exhibited greater hydrological resilience to logging compared to upstream areas,with attenuated runoff changes due to scale effects.Scale effects were more pronounced during dry seasons((-8.13 to -42.13)×10^(4) m^(3)·month^(-1))than in the rainy season((-11.11 to -26.65)×10^(4) m^(3)·month^(-1)).These findings advance understanding of logging impacts on hydrology across different spatiotemporal scales in subtropical regions,providing valuable insights for forest management under increasing anthropogenic activities and climate change.
基金supported by the National Natural Science Foundation of China (Grant No. 40774099, 10874202 and 11134011)National 863 Program of China (Grant No. 2008AA06Z205)
文摘It is still argued whether we measure phase or group velocities using acoustic logging tools. In this paper, three kinds of models are used to investigate this problem by theoretical analyses and numerical simulations. First, we use the plane-wave superposition model containing two plane waves with different velocities and able to change the values of phase velocity and group velocity. The numerical results show that whether phase velocity is higher or lower than group velocity, using the slowness-time coherence （STC） method we can only get phase velocities. Second, according to the results of the dispersion analysis and branch-cut integration, in a rigid boundary borehole model the results of dispersion curves and the waveforms of the first-order mode show that the velocities obtained by the STC method are phase velocities while group velocities obtained by arrival time picking. Finally, dipole logging in a slow formation model is investigated using dispersion analysis and real-axis integration. The results of dispersion curves and full wave trains show similar conclusions as the borehole model with rigid boundary conditions.
基金supported by China national petroleum corporation science and technology development projects(No.2011D_4101)
文摘The reservoir pore structure controls the reservoir quality and resistivity response of hydrocarbon-bearing zones and thus, critically affects logging interpretation. We use petrophysical data in three types of reservoir with different pore structure characteristics to show that the complexity of pore structure had a significant effect on the effective porosity and permeability regardless of geological factors responsible for the formation of pore structure. Moreover,, the distribution and content of conductive fluids in the reservoir varies dramatically owing to pore structure differences, which also induces resistivity variations in reservoir rocks. Hence, the origin of low-resistivity hydrocarbon-bearing zones, except for those with conductive matrix and mud filtrate invasion, is attributed to the complexity of the pore structures. Consequently, reservoir-specific evaluation models, parameters, and criteria should be chosen for resistivity log interpretation to make a reliable evaluation of reservoir quality and fluids.
基金sponsored by the National Science and Technology Major Project(No.2011ZX05023-005-006)
文摘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.
基金supported by the National Science Foundation of China(No.61102102,11134011,11204380 and 11374371)Major National Science and Technology Projects(No.2011ZX05020-009)+1 种基金Science and Technology Project of CNPC(No.2014A-3912 and 2011B-4001)Petro China Innovation Foundation(No.2014D-5006-0307)
文摘We designed a new downhole electrokinetic logging tool based on numericalsimulations and petrophysical experiments. Acoustic and electric receivers cannot be arrangedat the same depth, and the proposed composite electrokinetic logging tool offers a solutionto this problem. The sound field characteristics of the detectors were tested in a water tank inthe laboratory. Then, we calculated the sound pressure of the radiated acoustic field and thetransmitting voltage response of the transmitting transducers; in addition, we analyzed thedirectivity and application of the acoustic transmitting probe based on linear phased array.The results suggest that the sound pressure generated at 1500 mm spacing reaches up to 47.2k Pa and decreases with increasing acoustic source frequency. When the excitation signalsdelay time of adjacent acoustic transmitting subarrays increases, the radiation beam of themain lobe is deflected and its energy gradually increases, which presumably enhances theacoustoelectric conversion efficiency.
文摘The results of a heat-conduction experiment with a central point source in a sand barrel shows that the temperature of the heat source increase much faster in sand saturated with oil and air （dry sand） than in water sand. During cooling the temperature of the central heat source goes down slower in oil- or air-saturated sands than in water sands. Based on the theory of heat-conduction in porous media and the experimental results, we developed a new heat-conduction logging technique which utilizes an artificial heat source （dynamite charge or electric heater） to heat up target forma- tions in the borehole and then measure the change of temperature at a later time. Post-frac oil production is shown to be directly proportional to the size of the temperature anomaly when other reservoir parameters are fairly consistent. The method is used to evaluate potential oil production for marginal reservoirs in the FY formation in Song-Liao basin of China.
基金sponsored by the National S&T Major Special Project(No.2011ZX05020-008)
文摘Calculating the mineral composition is a critical task in log interpretation. Elementalcapture spectroscopy （ECS） log provides the weight percentages of twelve common elements,which lays the foundation for the accurate calculation of mineral compositions. Previousprocessing methods calculated the formation composition via the conversion relation betweenthe formation chemistry and minerals. Thus, their applicability is limited and the methodprecision is relatively low. In this study, we present a multimineral optimization processingmethod based on the ECS log. We derived the ECS response equations for calculating theformation composition, then, determined the logging response values for the elements ofcommon minerals using core data and theoretical calculations. Finally, a software modulewas developed. The results of the new method are consistent with core data and the meanabsolute error is less than 10%.
