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A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Petroleum Science 2024年第1期21卷 535-550页

作者： Song Deng Hao-Yu Pan Hai-Ge Wang Shou-Kun Xu Xiao-Peng Yan Chao-Wei Li Ming-Guo Peng Hao-Ping Peng Lin Shi Meng Cui Fei Zhao School of Petroleum and Natural Gas Engineering Changzhou UniversityChangzhou213164JiangsuChina Tubular Goods Research Institute of CNPC Beijing102249China CNPC Engineering Technology R&D Company Limited Beijing102206China

pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling *** methods for predicting pore pressure are limited when forming particular structures and *** this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore *** study collects data from three *** 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model *** this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure *** addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)*** IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training *** the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and *** achieves optimal performance on both training and test data,and the MLP model shows a high degree of *** indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.

关键词： pore pressure Grey wolf optimization Multilayer perceptron Effective stress Machine learning

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A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

Energy Geoscience

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Energy Geoscience 2024年第2期5卷 225-231页

作者： Goutami Das Saumen Maiti Machine Learning and Geocomputing Laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log pore pressure Machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

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Effects of confining pressure and pore pressure on multipole borehole acoustic field in fluid-saturated porous media

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Chinese Physics B 2024年第1期33卷 468-476页

作者：赵志强刘金霞刘建宇崔志文 College of Physics Jilin UniversityChangchun 130012China

In-situ stress is a common stress in the exploration and development of oil reservoirs. Therefore, it is of great significance to study the propagation characteristics of borehole acoustic waves in fluid-saturated porous media under *** on the acoustoelastic theory of fluid-saturated porous media, the field equation of fluid-saturated porous media under the conditions of confining pressure and pore pressure and the acoustic field formula of multipole source excitation in open hole are given. The influences of pore pressure and confining pressure on guided waves of multipole borehole acoustic field in fluid-saturated porous media are investigated. The numerical results show that the phase velocity and excitation intensity of guided wave increase significantly under the confining pressure. For a given confining pressure, the phase velocity of the guided wave decreases with pore pressure increasing. The excitation intensity of guided wave increases at low frequency and then decreases at high frequency with pore pressure increasing, except for that of Stoneley wave which decreases in the whole frequency range. These results will help us get an insight into the influences of confining pressure and pore pressure on the acoustic field of multipole source in borehole around fluid-saturated porous media.

关键词： confining pressure pore pressure fluid-saturated porous media multipole borehole acoustic field

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Research on Multi-Wave pore pressure Prediction Method Based on Three Field Velocity Fusion

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Journal of Geoscience and Environment Protection 2024年第6期12卷 269-278页

作者： Junlin Zhang Huan Wan Yu Zhang Yumei He Linlin Dan CNOOC EnerTech-Drilling & Production Co. Tianjin China

The optimization of velocity field is the core issue in reservoir seismic pressure prediction. For a long time, the seismic processing velocity analysis method has been used in the establishment of pressure prediction velocity field, which has a long research period and low resolution and restricts the accuracy of seismic pressure prediction;This paper proposed for the first time the use of machine learning algorithms, based on the feasibility analysis of wellbore logging pressure prediction, to integrate the CVI velocity inversion field, velocity sensitive post stack attribute field, and AVO P-wave and S-wave velocity reflectivity to obtain high-precision seismic P and S wave velocities. On this basis, high-resolution formation pore pressure and other parameters prediction based on multi waves is carried out. The pressure prediction accuracy is improved by more than 50% compared to the P-wave resolution of pore pressure prediction using only root mean square velocity. Practice has proven that the research method has certain reference significance for reservoir pore pressure prediction.

关键词： Velocity Field Resolution Machine Learning AVO Inversion pore pressure

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Prediction of the pore-pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix

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Computers, Materials & Continua 2024年第5期79卷 2907-2922页

作者： Xueya Wang Yiming Zhang Qi Liu Huanran Wang Mechanics and Materials Science Research Center Ningbo UniversityNingbo315211China Jinyun Institute Zhejiang Sci-Tech UniversityLishui321400China School of Civil Engineering and Architecture Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关键词： Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix

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pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality

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能源地球科学(英文) 2023年第3期4卷 252-265页

作者： Joshua Pwavodi Ibekwe N.Kelechi Perekebina Angalabiri Sharon Chioma Emeremgini Vivian O.Oguadinma Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIRDUGEISTerre38000GrenobleFrance TotalEnergies SA CSTJF Avenue Larribau64000PauFrance Centre of Excellence in Petroleum Geoscience and Engineering University of BeninBenin CityEdo StateNigeria Univ.Lille CNRSUniv.Littoral Cote d’OpaleUMR 8187LOGLaboratoire d’Oceanologie et de Geosciences59000LilleFrance

Despite exploration and production success in Niger Delta,several failed wells have been encountered due to ***,it is very essential to understand the spatial distribution of pore pressure and the generating mechanisms in order to mitigate the pitfalls that might arise during *** research provides estimates of pore pressure along three offshore wells using the Eaton's transit time method,multi-layer perceptron artificial neural network(MLP-ANN)and random forest regression(RFR)*** results show that there are three pressure magnitude regimes:normal pressure zone(hydrostatic pressure),transition pressure zone(slightly above hydrostatic pressure),and over pressured zone(significantly above hydrostatic pressure).The top of the geopressured zone(2873 mbRT or 9425.853 ft)averagely marks the onset of overpressurization with the excess pore pressure above hydrostatic pressure(P∗)varying averagely along the three wells between 1.06−24.75 *** results from the three methods are self-consistent with strong correlation between the Eaton's method and the two machine learning *** models have high accuracy of about>97%,low mean absolute percentage error(MAPE0.98).Our results have also shown that the principal generating mechanisms responsible for high pore pressure in the offshore Niger Delta are disequilibrium compaction,unloading(fluid expansion)and shale diagenesis.

关键词： Niger Delta pore pressure Reservoir Fracturing pressure Artifidal neural network Machine leaming algorithm Random forest regression

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Machine learning and data-driven prediction of pore pressure from geophysical logs:A case study for the Mangahewa gas field,New Zealand

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第6期14卷 1799-1809页

作者： Ahmed E.Radwan David A.Wood Ahmed A.Radwan Faculty of Geography and Geology Institute of Geological SciencesJagiellonian UniversityKraków30-387Poland Exploration Department Gulf of Suez Petroleum CompanyCairoEgypt DWA Energy Limited LincolnUK Department of Geology Faculty of ScienceAl-Azhar UniversityAssiut BranchAssiut71524Egypt

pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling *** pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure ***,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many *** machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is *** this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New *** predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)*** geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four *** ML methods achieved credible levels of pore pressure prediction *** most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four *** trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other *** two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 *** results suggest that these models can be used to predict pore pressure in nearby locat

关键词： Machine learning(ML) pore pressure Overburden Well-log derived predictions Overpressure

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Rock physics model for velocity–pressure relations and its application to shale pore pressure estimation

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Petroleum Exploration and Development 2023年第2期50卷 404-418页

作者： GUO Jingyi LI Min ZHUANG Mingwei SUN Yuefeng Department of Geology&Geophysics Texas A&M UniversityCollege Station77843United States Southwest Geophysical Research Institute BGPCNPCChengdu 610213China Institute of Electromagnetics and Acoustics Xiamen UniversityXiamen 361005China

Acoustic wave velocity has been commonly utilized to predict subsurface geopressure using empirical *** wave velocity is, however, affected by many factors. To estimate pore pressure accurately, we here propose to use elastic rock physics models to understand and analyze quantitatively the various contributions from these different factors affecting wave velocity. We report a closed-form relationship between the frame flexibility factor(γ) in a rock physics model and differential pressure, which presents the major control of pressure on elastic properties such as bulk modulus and compressional wave velocity. For a gas-bearing shale with abundant micro-cracks and fractures, its bulk modulus is much lower at abnormally high pore pressure(high γ values) where thin cracks and flat pores are open than that at normal hydrostatic pressure(low γ values) where pores are more rounded on average. The developed relations between bulk modulus and differential pressure have been successfully applied to the Upper Ordovician Wufeng and Lower Silurian Longmaxi formations in the Dingshan area of the Sichuan Basin to map the three-dimensional spatial distribution of pore pressure in the shale, integrating core, log and seismic data. The estimated results agree well with field measurements. pressure coefficient is positively correlated to gas content. The relations and methods reported here could be useful for hydrocarbon exploration, production, and drilling safety in both unconventional and conventional fields.

关键词： pore pressure pressure coefficient acoustic velocity bulk modulus pore structure shale gas Upper Ordovician Wufeng Formation Lower Silurian Longmaxi Formation Sichuan Basin

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Influence of pore pressure on tensile fracture growth in rocks： a new explanation based on numerical testing

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Frontiers of Earth Science 2015年第3期9卷 412-426页

作者： Shou MA Jianchun GUO Lianchong LI Leslie George THAM Yingjie XIA Chun'an TANG State Key Laboratory of Oil and Gas Geology and Exploitation Southwest Petroleum University Chengdu 610500 China SINOPEC Shengli Oilfield Company Dongying 257000 China School of Civil Engineering Dalian University of Technology Dalian 116024 China Department of Civil Engineering The University of Hong Kong Hong Kong China

The diffusion of pore fluid pressures may create both spatial and temporal effective stress gradients that influence or control the development and evolution of fractures within rock masses. To better understand the controls on fracturing behavior, numerical simulations are performed using a progressive fracture modeling approach that shares many of the same natural kinematic features in rocks, such as fracture growth, nucleation, and termination. First, the pinch-off breaking test is numerically performed to investigate the tensile failure of a rock specimen in a uniform pore pressure field. In this numerical simulation, both mechanical and hydrological properties of a suite of rocks are measured under simulated laboratory conditions. The complete tensional failure process of the rock specimen under pore pressure was reproduced. Second, a double-notched specimen is numerically extended to investigate how the water flow direction or pore pressure gradient influences the fracture growth. An exhaustive sensitivity study is conducted that examines the effects of varying both hydrological and mechanical boundary conditions. The simulation results indicate that local fluid pressure gradients strongly influence the state of stress in the solids and, thereby, fracture growth. Fracture and strength behavior is influenced not only by the pore pressure magnitude on a local scale around the fracture tip, but also by the orientation and distribution of pore pressure gradients on a global scale. Increasing the fracture growth rate increases the local model permeability and decreases the sample strength. The results of this study may provide useful information concerning the degree of hydrological and mechanical coupling action under geologic conditions.

关键词： pore pressure effective stress heterogeneous numerical simulation fracture growth rock

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Influential factors of loess liquefaction and pore pressure development

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Acta Mechanica Sinica 2005年第2期21卷 129-132页

作者： ChenggangZhao ZhenmaoYang FupingGao YunaZhang SchoolofCivilEngineering BeijingJiaotongUniversityBeijing100044China InstituteofMechanics ChineseAcademyofSciencesBeijing100080China

The liquefaction of loess under dynamic loading is studied experimentally with a dynamic triaxial test system. The effects of over-consolidation ratio (OCR), saturation degree and the frequency of dynamic loading upon loess liquefaction are investigated. The development of pore pressure within loess samples is also discussed. Based on the experimental results, the empirical relationship between pore pressure ratio and loading cycle number ratio is established for normal consolidated saturated loess.

关键词： Loess liquefaction Over-consolidation ratio Loading frequency Saturation degree pore pressure

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