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Hello,
I recently started my PhD trying to apply machine learning in reservoir petrophysical properties prediction. I am a computer science major trying to connect with Petrophysics. However, I am having a challenge knowing the current problem being faced in the industry that be my main research. From the investigation I have done so far, all the problem i could think of have already been solved. I am trying to find a current challenge or problem that is being faced in the oil and gas industry in reservoir petrophysical properties evaluation.
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I suggest you look into the problem of how to reliably estimate permeability from NMR data. The NMR log measures pore size distribution and in clastics this can be used ( with a bit of caution and proper calibration) to fairly reliably estimate permeability. In carbonates this is much more difficult. This due to the commonly much more varied pore size distribution in carbonates ( as compared to clastics). Given that permeability is controlled by pore throat size rather than pore size as such, there is in carbonates rarely a clear relationship between porosity and permeability.
RELIABLY solving this issue would be very useful tot the Industry
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In the oil and gas industry, for technical, economic, and similar reasons, well-Log running is done from special intervals. Therefore, to build comprehensive models for field development, we will need more information at different depths. Today, with advances in numerical methods, especially machine learning and deep learning methods, we can use their help to eliminate these data gaps. Of course, there are methods such as rock physics that are very practical. But according to my results, part of which is described below. It is better to combine the rock physics method with the deep learning methods, in which case the results will be amazing. I selected wells from the Poseidon Basin in Australia for testing and got good results. In this study, by combining the rock physics method and deep learning (CNN + GRU), the values ​​of density, porosity, and shear wave slowness were predicted. A comprehensive database of PEF, RHOB, LLD, GR, CGR, NPHI, DTC, DTS, and water saturation logs was prepared and used as training data for the wells. The below figure is the result of a blind well test for Torosa well in the Poseidon Basin, Australia. As you can see, the prediction results are very close to the measured values ​​of shear wave slowness in this well.
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You can use Machine learning algorithm to learn the data trends, where it is available and what parameters the data depend on, then use the algorithm to predict the data where they are missing the relationships learn from the training.
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  • To estimate suitable petrophysical properties, would it will be useful to use CNN or GRU algorithms alone?
  • Or, will this estimate will be appropriate when the two methods are combined?
  • Convolutional_Neural_Network: In deep learning, a CNN is a class of artificial neural networks, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. Counter-intuitively, most CNN's are only equivariant, as opposed to invariant, to interpretation.
  • Gated_Recurrent_Units: GRU is a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.
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Dear Erfan Rahimi . See the following useful RG link:
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Petrophysical and seismic data analysis is one of the key technics for reservoir characterization and pore fluids identification. Rock physics is a link between these data and rock properties, such as porosity, lithology, and pore fluids. Quantitative interpretation and risk assessments of data and uncertainties associated with predictions need methods and multidisciplinary tools that use statistical technics and pattern recognition approaches, in addition to deterministic rock physics relations. The statistical rock physics approach is a way for quantifying the uncertainties in different steps of reservoir exploration and management. This approach applies some of the progressive statistical methods such as the Bayesian approach, Monte Carlo simulation, and Information Entropy. In addition to quantifying the associated uncertainties with predictions and evaluations, the statistical rock physics approach is a useful method to identify the most valuable information for predicting the desired properties.
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I'm currently focusing on research, which covers topics such as geomechanics, geophysics, statistics, & numerical modeling techniques.
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One of my challenges in this research is a gap in log well information in some formation intervals.
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Given the time constraints, how can these data gaps be filled with the least amount of uncertainty?
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Hello;
I- In general cases, we can apply many statistical tools and machine learning methods to reconstruct the missing data from other measurements. (Handling missing values in exploratory multivariate data analysis, see for example this link https://husson.github.io/index.html
2- There is a special study branch for data analysis for missing data.
3- It is known that real data may have missing sections due to the acquisition environment or to the lack of measurement. In such cases, rock physicists often proceed to reconstruct the missing data from other measurements.
For this purpose, the Rock physics module provides the following tools:
  • Gardner approximation: based on either an exponential or polynomial approximation, you can reconstruct bulk density from compressional slowness and reconstruct compressional slowness from bulk density
  • Note: Based on the Mavko et al Rock Physics handbook, the maximum polynomial degree used in the approximation is 5.
  • Faust approximation: this method is used to reconstruct compressional slowness from resistivity data.
  • The Gardner approximation provides a synthetic bulk density log reconstructed from a P-waves velocity Vp or compressional slowness log. Two alternative approaches have been implemented for this purpose: the exponential and the polynomial method.
  • The Gardner approximation for the compressional velocity reconstruction Vp from the bulk density RHOB is used to get synthetic compressional velocity and compressional slowness logs.
  • The Faust approximation for the compressional velocity reconstruction Vp from the resistivity Ro is used to get synthetic compressional velocity and compressional slowness logs.
  • Han's empirical Vp and Vs: This method computes compressional and shear velocity based on the empirical relations developed by Eberhart-Phillips (1989) on Han’s data.
  • Display Han's Vp vs Phi : This method generates a parametric cross plot where compressional velocity is displayed versus porosity for the considered depth points. Parametric Han regression lines linking the compressional velocity to porosity are displayed for different clay contents. These lines are based on the work of Eberhart-Phillips (1989).
  • Display Han's Vs vs Phi This method generates a parametric cross plot where shear velocity is displayed versus porosity for the considered depth points. Parametric Han regression lines linking the shear velocity to porosity are displayed for different clay contents. These lines are based on the work of Eberhart-Phillips (1989).
  • Smooth with missing values, corresponds to a Gaussian smoothing in which the missing values are taken into account to calculate the weighted average and impact the calculation of the smoothed value. It is a way to smooth point data as they usually contain a lot of missing values.
  • For example, the K.mod method performs parameter prediction and log reconstruction using multilayer perceptron technology. Dedicated templates enhance usability. Quantitative estimates of the fit quality obtained are provided. Models are stored and applied to other wells.
I hope I have given you some help and good luck.
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For a matter of SWT, i need to generate a Density log from the Sonic log that i had. How can i do it ?
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You may Gardner equation between density and velocity
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Dear All,
I am currently looking for different international examples (papers) of soviet-type/Russian-type geophysical well-log interpretation and application.
This type of archival, geophysical devices were widely used mainly in Europe and Asia in the 1960s-90s. The main difficulty is the proper standarization of this quite specyfic kind of measurements.
I have already found some examples from Poland, but I would like to apply my research to a broader, international perspective. I cannot find any examples from any different area even though I know that these measurements have been used in many countries around the world.
I would really appreciate your help.
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Dear Sara, During the 1990s I was among a group of geoscientists working in the former Soviet Union who produced a book called "Russian Style Formation Evaluation", which was published jointly by the London Petrophysical Society and the Geological Society of London. It was a guide to using Soviet-style well logs. It in now out-of-print, and available second-hand on-line but quite expensive, although you may find it in a library. I've worked a lot with Soviet-style logs, but on a commercial basis so no papers published I'm afraid. Regards, Graham
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How petrophysics can complement magnetic data discussing of structural geology, mineralisation, geology lithology
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Well logs and petrophysics complement magnetic data for geological mapping
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Hi,
I hope someone can help me. In some wells I have DTSM_Fast, DTSM_Slow, and DTSM. But in some wells, I have only DTSM_Fast and DTSM_slow - no DTSM. Is it possible to calculate DTSM from Fast and Slow? I thought it should be a standard output from advanced shear anisotropy processing (DTSM, DTSM_Fast, DTSM_slow, Stoneley etc.) but some logs, like DTSM, are missing. So now I'm just confused.
I know it isn't just a simple average of DTSM_fast and DTSM_slow because the result doesn't match the DTSM log.
Can you advise, please?
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The slow shear wave is created either in conditions of intrinsic anisotropy due to shales, layering, fractures (homogeneous anisotropy) or in conditions of stress-induced anisotropy (inhomogeneous anisotropy).
A dipole tool, run in cross-dipole mode, will record 4 shear wave signals 90° apart (DTSM_XX, _XY, _YX, _YY), not necessarily aligned with the directions of the fast and slow shear waves.. The Alford rotation is one process used to reconcile the 4 signals and to determine the speed and direction of the fast and slow shear waves (DTSM_FAST, DTSM_SLOW).
The DTSM curve is ambiguous and necessitates the knowledge of the parameter DTSS (source of shear sonic slowness).
In the case of DSI tool, DTCS can take any value between DTRS (monopole shear slowness receiver array), DTTS (monopole shear slowness transmitter array), DT4S (average of DTRS and DTTS), DT1R (lower dipole shear slowness raw), DT1 lower dipole shear slowness dispersion corrected), DT2R (upper dipole shear slowness raw), DT2 (upper dipole slowness dispersion corrected).
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How to simulate the weathering progression of the stone monument ?!, taking into account the presence of different parameters: different lithology, localization of stone, side exposition, insolation, petrophysical and petrographical properties?
and in the same monument.
what is the best approach, in this case, a multivariate study or a bivariate study
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thank you very much, Mr. Harald, of your suggestion. it's very helpful
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Hi,
I am working on the petrophysical modeling of the sandstone reservoir suing Petrel. I have selected three facies i-e sandstone, shaly sandstone and shale on the basis of basic log analysis which is used to prepare facies model. 3D model of the estimated petrophysical propertes i-e porosity, permeavility, water saturation etc have been also prepared.
How I may incorporate the effect of facies (Facies Screening) in my prepared petrophysical model?
What really is meant by facies screening in 3D models?
How I may understand this concept in a clearer way? I have tried to read manuals but unable to execute the process in petrel.
Thanks a lot for your time.
Regards,
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Dear Atif,
I have also worked on Petrel 2015 to prepare 3D models for Dhodak field and prepare 3d model of facies, porosity, water saturation and volume of shale.i have worked on Pab Sandstone of cretaceous age and thsi is my Mphil thesis
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As the title, where can I find a relation describing the compressibility and permeability of sedimentary rocks? I am trying to use it as a first guess of initial condition in numerical modeling of sedimentary formations (many layers).
Thank you.
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Well,
The proportionality constant in Darcy's law depends on the porous medium and the quality of the liquid, ie, k is a function of the medium and the liquid. If we keep ∆h, ∆l constant and make two attempts first using the same sand, liquid and water, and in the second we use the sugar solution, we find that the value of v will be greatly reduced On the second attempt it up on the first attempt.
Experiments have shown that if the porous medium contains regular spherical granules, the radius of d affects the value of v and shows that v Darcy speed is proportional to:
- Directly with a square radius of porous medium granules. - directly with the specific weight of the liquid Ɣ = ρ γ
     - And inversely with the kinematic viscosity of liquids (μ) and that the stability of the hydraulic tilt.
We can write these relationships as follows:
              V α d2
              V α ρ g
              V α 1 / μ
According to Darcy notes: V α dh / dl
السابقة Past relations of proportionality lead us to another version of Darcy law:
V α [d2 ρ g / μ] dh / dl
V = [C d2 g / μ] dh / dl …… .1
Where C is a proportionate constant and depends on the properties of the porous medium that affect the flow such as the irregularity of the radius of the particles of the formation of the medium and the irregular distribution and degree of rotation and compacting conditions.
equation 1 with the Darcy equation 2: V = K dh / dl …… .2
► Deduce equation 3: K = [(Cd2 g) / μ] …… 3
Μ Since μ, ρ depends only on the liquid, that is, each is a function of the liquid only.
        Cd2 depends only on the media, ie, it is a function of the medium, from which we deduce equation - 4 ..... k = Cd2 -------4
         - From equation 4 in equation 3 we find that K = (k ρ g) / μ
► where k expresses specific or intrinsic permeability
► If we refer to K with hydraulic conductivity, it is better to call k permeability.
It should be noted that some references use the term permeability coefficient instead of the term hydraulic conductivity hydraulic conductivity.
              Permeability is a function of the medium only and has an area of ​​m2 and the term is used in petroleum industries where there are gas, oil, and water in a mixture flow system and when measurements in square meters k will be very small, so petroleum engineers have defined the permeability unit, Darcy. If we replace the value of K according to the equation (= (k ρ g) / μ K) and place it in the Darcy equation (V = Ki), the Darcy law becomes.
V = (k g) / μ. dh / dl
Regards
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We are looking for a cheaper alternative/addition to Petrel Schlumberger.
We have several Petrel licences, but our capacities have grown lately so we are looking for something new.
Software should be compatible with Petrel, and we aim to use it for well correlation, quantitative seismic, seismic interpretation, geological modeling...
We would be grateful if anyone shared experience on this matter!
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There are different segments in petroleum software key ones are seismic interpretation / geomodelling / simulation & pre/post. For geomodelling key actors beyond SLB are Emerson with IRMS and Paradigm with Skua. For simulators TNavigator/Echelon/Imex[Stars/Gem]. There are also niche product for a myriad of smaller subjects (well test, near well bore flow, log interpretation, etc.). What matters in operations is ease of use, ability to move data back and forth, coherency of functionnalities, sophistication (pure performances), expandability and costs. Research ususally put expandability (and I/O) first. What are your drivers?
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I'm trying to search if there are correlations/formulations to determine PSD from OH PP interpretation. Which later can be calibrated to actual PSD from core data.
Appreciate your advice.
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pore size distribution and its effects on rock physics
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Hydrogeology
Hydrology
Petrophysics
Well logging
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it is illegal way
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Hi Everybody, I am seeking guidance to make different realisations (property distribution) in an already developed petrel model. The purpose is to observe the effect of differently distributed properties e.g., porosity, permeability (heterogeneity) on fluid flow. Let’s say, make 10 different realisation and plot their results after simulating in E100 or E300.
Thanks
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Dear Adeel Sohal,
To generate multiple realizations of reservoir property distribution, you need to use conditional simulation through either Sequential Gaussian Simulation or Sequential Gaussian Random Field Simulation, or Sequential Gaussian Co-Simulation (if you have a secondary variable).
You can specify the number of realizations in Petrel and also use some quantification to rank these realizations, such as IOIP for porosity simulation.
Hope this helps!
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I am a research student just confused about the workflow for my project.
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Dear Nofal Munir , you can correlate reservoir property (especially porosity) with diagenetic process. For more advance diagenetic, you can elaborate with clay diagenetic stage (illitization). If you have spectral gamma ray log (thorium, uranium, potassium) you can make clay type (I had published on my research page).
In other ways, diagenetic also has influence for grain density value to make petrophysical input. Let me know if you want to discuss
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Please, tell me your opinion.
Who are the world top scientific experts/groups in advanced seismic interpretation techniques in terms of the subsurface characterization? I am talking about people that are defining the state of the art and who are working on the cutting edge of this technology.
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Joe Cartwright, Henry Posamentier, Lesli Wood, Kurt Marfurt.
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Hello
How to make crude oil by combining different materials, such as gasoline?
Also, can i determine API gravity (American Petroleum Institute) for this Crude oil?
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Hi Sajad
Your question is quite complex. I recommend you start designing the crude starting from a mixture of products that have high content of saturated and aromatic fractions, and then subjecting this mixture to thermal cracking reactions in Batch type reactors and an inert atmosphere between 250-500 psig of Nitrogen. You should create a training matrix so that you can vary the temperature and residence time conditions, since they are the variables that affect the quality and performance of thermal conversion products. Also I present one of my articles to have a better idea about this type of process (thermal cracking) and characterization and see if you can apply, this in my profile. Construction of a formation matrix for the production of anode-grade petroleum tar pitch by thermal cracking. Once you obtain a viscous product you can use ASTM D287 - 12b Standard Test Method for API Gravity of Crude Petroleum and Petroleum Products (Hydrometer Method)
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Importance of these in the Petrophysical Interpretation.
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Many thanks Emad, Zehui & Julius.
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I am trying to discuss the result of the realizations generated from the volumetric estimation of hydrocarbon in place and I determined the mean and standard deviations of the three cases for each reservoir studied. I need assistance of the what the mean and standard deviations will imply geological to make a good discussion.
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Many thanks Al-Mudhafar. I actually did.
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Actually I'm working in my dessertation , so I need some help in order to interpret my logging. 
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Ali Altemimi, Actually sonic log is measurement of time taken by sonic wave to travel unit distance, it is represented as microseconds/feet  on log. Since sonic wave has different speed in different materials, like sonic wave has lower speed(higher travel time) in water/fluid, then in pure sandstone. A reservoir rock has porosity which is filled with water or hydrocarbon, will have higher travel time then the rock with no or less porosity(non-reservoir). This is the reason when sonic value decreases on log in compact rocks(non-reservoirs).
There is a contradiction when non-reservoir rock is shale, in case of shale also sonic log reads higher value. It is due to shale contains micro-porosity which is filled with water.  So, sonic value is high in shales also.
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if mudstone overlaps with sand, and the porosities that favour production overlap the vertical boundaries, in this case the reservoir will be the sand, and will also include the mudstone, assuming it has moveable hydrocarbons? if yes, what does this mean for the GRV and Net-To-Gross (NTG)?
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Thanks so much, I really appreciate your answers. How can the drive mechanism in an unconventional reservoir (shale) be classified best? 
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1.What exactly sand fraction means?Is it 1-Volume(shale)?
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Dear Sde De,
Please find the attached HANDBOOK. You may get an idea about geological evaluation, lithology and mineralogy.
Regards
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I am doing Petrophysical analyasis  of a well in Farsi Block (offshore) located in the Iranian water of persian gulf with in western salt basin.
General lithology of the reservoir is Limestone and Dolomite.
There is no core data available.
I couldn't find any other petrophysical study in the same area. 
I am struggling to decide Water Saturation equation that can be used in this area.
Any lead would appreciated.
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Badal,  Fractured carbonates can be very tricky.  One large fracture can mean access to high reserves, yet remains really difficult to quantify in terms of water saturation - because there is none.  You need to look at the type and dispersion of the fracture system.  There are a number of SPWLA papers that have dealt with this, but in the extreme case it is simply a binary solution.  Sorry that I can't give you a "use this equation, you'll be fine" answer, because there is no single relationship that covers the vast range of fracture options.  Using formation fluid gradients, if that information is available, is probably as good as you're going to get.
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which are th processes that, apart of hydrate dissociation and formation influence the chlorinity profiles in natural gas hydrate systems?
1. Fluid advectio from below? (Which fluids?)
2. Sea level fluctuations? (which is the impact)
...
Is there any available and easy model ?
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First things to consider are advection and diffusion, and there are old (but not necessarily simple) models for chlorinity profiles in gas hydrate systems. See Davie and Buffett (2001,2003, in JGR and EPSL)
Sedimentation and compaction implies that pore water moves with respect to the solids, even in the absence of an external fluid source. Both hydrate distribution in the sediment and choride concentration are influenced by this process.
Fluid advection from below can occur, and seawater salinity varies during glacial-interglacial cycles. For instance, both processes where observed at Blake Ridge (see Egeberg and Dickens, 1998). Seawater is more saline during glaciations because there is more ice on the continents, and this is reflected in chloride advection-diffusion profiles worldwide (See Adkins and Schrag, 2002).
External fluid sources often invoked are clay and organic matter diagenesis. On the other hand, alteration of volcanic glass (that may be present in the sediment) into clay (dominantly smectites) takes up pore water. Volcanic ash diagenesis in the sediment (as well as basalt alteration in the oceanic crust) can thus increase in situ chlorinity at low temperature while smectite/Illite reaction will lower chlorinity as temperature increase (e.g. Henry and Bourlange, 2004). These processes happen beneath the gas hydrate stability field but can influence pore fluid composition in the shallow sediment by advection-diffusion.
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What are the impacts of gas distribution (continuous vs patchy) and data acquisition (i.e. well log vs seismic)?
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Vp and Vs velocities can be very low in uncosolidated sediments, especially in case of partial saturation. Dispersion and strong attenuation can also be noticed at ultrasonic and seismic frequenciesespecially in case of patchy saturation (in the sense of White). Some effective models based on double porosity theory allow to handle this kind of mechanisms (see Pride et al, 2004). In very extreme cases, anelastic effects can also be observed.
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i'm going to extract some fractal parameters from a petrophysical signal (well-log) using wavelet transform, i'll be glad if you help me to how choose the suitable mother wavelet for this purpose
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Each wavelet type is suitable for the signal decomposition (for full decomposition with the scaling function). The fractal dimension what you want to estimate, could relate mostly to the layer structure imaged by logs, therefore the most simple Haar-wavelet base may give the most efficient decomposition, because Haar-system reflects the layer thickness distribution.
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I am working on a reservoir simulation project and I am wondering why do we need to provide the data for the capillary pressure to the simulator? 
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Actually, that is not really correct because you argue with a pore scale picture while the reservoir simulation uses Darcy scale concepts.
You need to include capillary pressure into the simulation in all cases where capillary forces are comparable or larger than the viscous forces. That is the case for instance in the a transition zone, because there the saturation is strongly influenced by capillary pressure. In other cases capillary pressure might not be important, i.e. In many waterfloods it may not be needed. If in doubt, include it (as a guess value, using reasonable parameters) in your simulation and see if it makes any difference. But you have to make sure to use the right imbibition or drainage pc.
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I really need to scale down the rate of injection from field units to laboratory units.
Thanks.
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Christian,
this is a very good question - but very complicated: You should follow other modeling experiments in hydrodynamics and keep attention on the Reynolds Number. The problem is that you have too much different parameter like pressure, velocity, viscosity, permeability and adhesion. I am dreaming since years from "a" Reynolds Number which helps to transform down hole conditions to lab conditions like you do it in aerodynamics with flow models. Just try to eliminate and reduce the number of parameters and buy a very big computer !! Good Luck.
Regards
Paul
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I have petrophysical properties of a core included I.O.I% which is about 10% and I wanna use the results to model a geochemical mechanism.
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Tank you guys.
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methods or papers are well accepted :)
I'm interested in particular on fracture filling hydrates
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Archie's equation remains the best option for estimate saturation from resistivity logs
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- Measuring porosity from the core Samples.
- Estimating porosity form the satellite image analyses.
- Estimating porosity form the field studies by measuring the fractures dimensions.
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Re: Gheorghe Branoiu; Thanks a lot, for your support, that are very nice literature.
I highly appreciate your answer.
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Thanks in advance for your replies.
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Dear Shib, it depends on which kind of measurements (and scale) you refer. Elastic moduli, porosity, permeability...? In addition you must also to take into account if you're referring to laboratory scale (properties on small samples) or in situ scale (properties at Earth surface, in depth, tectonics...). In general terms main uncertainties in rock physics depends on anisotropy and inhomogeneity (of the rock) and on accuracy of instrumentation used.
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i need to construct an equation to calculate the heat flow from the lithology by the aid of will logging
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Well, heat flow can be calculated from knowledge of rock thermal conductivity (TC) and  the temperature gradient (gradT) for a specific depth interval (applying Fourier's law or the Bullard method using thermal resistance). But in general never from knowledge of lithology alone.
Well log data can provide information on rock TC. For a brief review of the basic approaches and for a set of equations to derive rock TC from standard well logs using universally applicable equations for sedimentary rocks, please check out link 1 (Well-log based prediction of thermal conductivity of sedimentary successions: a case study from the North German Basin). An alternative approach but usefull approach if you have control of lithology and mineralogy is used by Hartmann et al. (2007) -> link 2
Once you have rock TC you need information on the gradT. Do you have any temperature information (BHT, DST, logs) from boreholes that allows you to construct temperature gradients?
If nothing else is available, an alternative but very rough approach can be the use of empirical equations between radiogenic heat production and heat flow. I know such kind of relations from the Canadian shield or from Norway.
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Esteemed Colleagues,
Does anyone have suggestions about couplants for ultrasonic transduers at high PT? Apart from using Au or Pt foil, are there any polymers (?) or other high viscosity materials that will not breakdown at high temperatures? Most of the products I have come across breakdown above 200 degC.
Also, and this is for the vintage generation like me: what was the chemical formula for the couplant V9 made by Dow Corning? It gave beautiful shear signals!
Mahalo,
Manika
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Did you try pressure coupling?
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Hi, 
I have issues applying boundary conditions to my wellbore model. In order to apply principal stress I created a 3d model. Shall I use solid mechanics or Shell for my study, My wellbore shows high strain even at low pressure. Please take a look at model attached, any advice would be appreciated.
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Hi Pooya,
Thanks for your response . Have attached my model with the problem. Please advice.
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Grain boundaries/bulk.
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Well, with regards to petrophysics, you can calculate porosity by using Archie's law (easy to google).
Good luck!
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I am currently looking for some papers concerning Euler connectivity with specific application to Digital Rock Physics. (I am not looking for papers explaining the basics of this topic, but specifically for applications of this!)
Does anyone have some good suggestions? Especially looking for sandstone related topics. Thanks in advance!
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Well, the question was about Euler Conenctivity, not Hamiltonian Graph or Euler Graph Theory, sorry. :-/
But maybe you can specify, what you exactely suggested?
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I do need some more information on a sandstone called "Idaho Gray" (link where it was bought attached), especially related to geological setting, formation, depository. If available, also concerning petrophysical investigations.
Thanks for any tipps & hints!
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As promised, here the document!
Thanks again to all for any information!
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I would like to compare the mean values of petrophysical properties for the same stratigraphic interval at different borehole locations. These properties are determined using empirical regression equations and are assumed to be valid and applicable. Usually, if the preconditions are met, I would apply, e.g., an independent t-test to compare the means of both groups. However, by doing so I am not able to consider the uncertainty in the determination of the single group values (resulting from the application of the regression equation that should allow prediction of the target properties with a mean error ranging between 5 to 15 %). I want to avoid having a false positive result in the comparing-mean test (that means, seeing a significant difference between both means although this results from the uncertainty of the prediction equation and not from real significant variations between both groups). How can I circumnavigate this problem? Does anyone have an idea? Raw data and all statistical parameter of the applied regression equations are available to me. Big thanks in advance.
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Normally your prediction variability is compraised in the distribution of the observation. However, I think there is a possibility to calculate the distribution of your mean predicted by the two models using the bootstraps technic.
I hope I could help you.
Best regards
Gregor
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Dataset for different well logs records in unconventional reservoirs.
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A good idea is to develop your work based on data reported from conventional source rocks, they can potentially serve as shale plays. Plenty of data exist from the Kimmeridge Clay Formation or its equivalents in North Sea.
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I need help to identify the breakthrough of injected water having different salinity than the formation water.
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It is not easy in an oil well! Oil is a dielectric and standard conductivity-salinity relations (PSS-78, TDS) for this medium (saltwater/oil) doesn't work properly. Plus electrodes in a contact (conductive) conductivity cell will be deteriorated in a presence of oil. I would recommend to use CT probes with an inductive (electrode-less) conductivity cell (for example: http://www.rbr-global.com/products/ct-and-ctd-loggers) and determine own conductivity-temperature-salinity scale in oily medium.
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I want to carry out this research, but I have the limitation of data sets. I can get logs and the seismic data, but not the cores/petrographic data. Can anyone suggest what methodology to adopt with this data limitation?
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You should be able to do a lot with logs and seismic data. I'm assuming you have a basic log suite including gamma ray, resistivity, spontaneous potential, neutron, and density logs. From logs alone, you can do quite a bit of log analysis and petrophysics including net to gross calculations using gamma ray and/or resistivity cutoffs, relative reservoir quality delineation using resistivity and density, water saturation, and getting an approximate sense of fluid type with the neutron-density logs. All that said, the log response of tight gas sands can vary considerably. Some tight gas sands I am familiar with here in Colrado have no appreciable gamma ray or resistivity signature: density is the only good delimiter of the reservoir.
Regarding the calculation of gross in-place hydrocarbon estimates, you can use an equation involving porosity, water saturation, areal extent, stratigraphic thickness, irreducible water saturation, and recovery factor (if necessary). If the reservoir needs stimulation to produce, an estimate of effective frac radius will suffice for the reservoir dimensional properties I previously mentioned.
It would also be good to plug your estimated or calculated ranges for those reservoir parameters into Crystal Ball, @Risk, or some other probabilistic modeling package. It will help you understand the sensitivities of your parameters and the range of your uncertainties (e.g., the P10:P90 ratio).
Seismic data will allow you to make a structural model of the reservoir, understand faulting and general structure that can affect both migration over geologic time and faults as geologic hazards to drilling and completions. This assumes your gas sand is thick enough to be detectable with seismic. If not, the seismic is still useful for fault mapping.
Best of luck.