Reservoir Engineering - Science topic

Suresh Kumar Govindarajan Although artificial intelligence (AI) has the potential to make a significant contribution to the comprehension and reproduction of reservoir physics, there are some restrictions that need be taken into account.

In reservoir physics, fluids, rocks, and their interactions in subterranean reservoirs are all subject to complex behavior. AI can help with data analysis, pattern recognition, prediction, and optimization, among other reservoir physics-related tasks. Large-scale data processing, pattern recognition, and prediction capabilities are all capabilities of AI systems.

In reservoir characterization, where it can examine seismic data, well logs, and other geological information to produce precise models of underground reservoirs, AI can be especially helpful. AI can assist in identifying important reservoir parameters like porosity, permeability, and fluid by applying machine learning approaches.

It is crucial to remember that AI models are founded on statistical correlations and patterns acquired from past data. models may not completely represent the underlying physical processes, even while models can capture and mimic some features of reservoir physics. It can be difficult to properly mimic reservoir physics using AI models alone since it incorporates a variety of very complicated and non-linear phenomena, including fluid movement, multiphase interactions, and geomechanically impacts.

As a result, even while AI can aid in reservoir characterization and management and provide useful insights, it should only be used in conjunction with conventional physics-based models. The accuracy of predictions can be increased and reservoir behavior can be better understood by combining physics-based models with AI techniques.

To calculate the molecular weight and specific gravity of a mixture or composite material from the molar composition, you can use the following steps:

Please note that this method assumes ideal mixing behavior and that the molecular weights of the components are additive. It may not be accurate for mixtures with significant interactions between the components or for systems with complex molecular structures. In such cases, more advanced methods, such as using equations of state or experimental measurements, may be necessary.

Mohammed Qassim

Thank you for your question. Yes, it is possible to use Petrel in conjunction with Python for various purposes, including creating plugins and custom workflows. While Petrel does not have a direct Python integration like it does with MATLAB or C# in Visual Studio, there are alternative approaches you can consider.

1. Ocean for Petrel: Ocean for Petrel is a software development framework provided by Schlumberger, the company behind Petrel. It allows developers to extend the functionality of Petrel through custom plugins and workflows. While Ocean for Petrel primarily supports .NET languages (such as C#), you can still leverage Python in combination with .NET by using IronPython, which is an implementation of Python that runs on the .NET framework. IronPython allows you to write Python code that can interface with .NET libraries and, consequently, interact with Petrel through the Ocean for Petrel framework.

2. Python.NET: Python.NET is another option that enables Python and .NET interoperability. It provides a bridge between Python and .NET, allowing you to use .NET libraries within Python code. With Python.NET, you can potentially create plugins or custom workflows for Petrel using Python as the programming language. This approach offers flexibility by leveraging the power of Python while integrating with Petrel through the .NET framework.

Both the Ocean for Petrel framework with IronPython and Python.NET offer possibilities for using Python in conjunction with Petrel. They enable you to access Petrel's functionalities, develop custom workflows, and create plugins using Python code.

It is worth noting that the specific implementation details may depend on the version of Petrel you are using, as well as the requirements of your project. Therefore, it is recommended to refer to the official documentation and resources provided by Schlumberger to ensure compatibility and obtain detailed guidance on integrating Python with Petrel.

I hope this information helps you explore the options for using Python with Petrel for creating plugins and custom workflows. If you have any further questions or need additional assistance, please feel free to ask.

First several setups might exist. In some of them (e.g., so called PetrelStudio), the data is not in the Petrel project but rather in a database elsewhere and the project only reference data with or without cache. In the following I will focus on (quite common) case in which the data is in the Petrel project. In this case, the file to data correspondance is not transparent nor bijective. The formats are not explicit and not straightforward. You will have to access via Petrel and export your data. There could be exception in which you might be inposition to reverse engineer format and file allocation, but this would typically be very rare exception and unreliable. If the problem you face is lack of access to petrel, you are in a bad spot. If it simply not knowing how to do it, then it usually takes a bit of trial but export is typically doable, reasonnably trusworthyt for accuracy and completeness and can be automated. It certainly is possible to transfer most of the data to something that can bea read in CMG suite with anot too much (but not zero) reformatting efforts. Am I clear?

Hi Sulaimon,

In principle what appears to be a simple question, but there are a couple of obstacles on the road. If we are certain that we have genuine gas, oil and water pressures recorded at known tvd depths and we do know that both the gas-oil or oil-water system is in capillary drainage equilibrium plus that you have checked that your gas, oil and water hydrostatic gradients does not change with depth, then the answer is find the intersection depth (FOL) between the gas and oil gradient and subtract the capillary entry height for gas into oil or find the intersection depth (FWL) between the oil and water gradients and substract the capillary entry height for oil into water.

However, this was the easy solution and in the real world reservoir fluids are rarely in perfect capillary drainage equilibrium, gas/oil/water gradients as recorded by e.g. an MDT-log are rarely constant with depth, a MDT-log do often not record the true gas, oil or water pressure due to wettability issues etc. So in reality the simple solution is rarely correct and you need to include the effect of how you measure the pressure (wettability effect) and burial/charging history in your analysis.

So the real world is much more complex than the ideal situations we typically assume.

Have fun.

Regards

Finn Engstrøm

Senior Petrophysical Advisor

Thank you Mehdi Razavifar jan.

Pierre Bergey Thank you so much for your kind reply. I did what you mentioned and it works. Thank you

The costs involved in building, calibrating and using other types of forecast models (3D reservoir dynamic simulation models, data driven machine learning models) are such that a large volume of reserves (most of the mature fields and a large fraction of remaining reserves) are derived from DCA. One can however surmise that streamlining of machine learning usage will enable such approaches to overcome simpler form of data driven approaches like DCA. In my opinion, these approaches will not overcome 3D reservoir models for green (or immature) fields any time soon.

Reservoir temperature in accumulations depends on their occurrence depth and geothermal specifics of the giver Earth crust area. Observed temperatures vary from near 0°С in gas-hydrate accumulations to hundreds °С in deep-lying formations

Dear Gift Dembetembe

I'm completely agree with Imoukhuede Idehai

Try King Fahd University (KFPUM) -KSA

Not sure there is such title as P.Eng in Ontario but definitely a PE status is recommended regardless of your engineering discipline. Canada is highly regulated environment for engineering practise whether in a corporate employment or as an independent consultant. All provinces have their own licensing authorities with different requirements. Without a license, an engineer must work under the supervision of a PE who must certify or sign-off his work, reports or design. To be licensed, an engineer will have to go through series of engineering and professional certification exams (depending on level of experience some can be waived but not all) to be licensed to practise professionally in Ontario. Visit Professional Engineers Ontario (https://www.peo.on.ca/) for details.

Hassan Mahani I saw you mentioned one the works of Alan S. Emanuel from Chevron. Back in 1970s, Emaneul had published remarkable works in the area of commercial simual. In fact, in the first year of my Ph.D. I was totally stuck, and I only figured out the general idea of my PhD work after reading one of his papers ( )

It was interesting to me to see his name here. If I am not wrong Emanual has around 30 papers in SPE library.

The fact that there is no gas in your system (meaning low compressibility) and the small description you provide suggest that your are dealing with a small closed (no analytical or explicitly gridded aquifer) low compressibility reservoir. In such situation and unless you have setup a pressure control of your injection or production or both, seeing strong pressure variations is normal. You would need to make the injection or production or both controlled by a regional pressure.

I am not sure what you mean by "provide an example". These are inflatable packers with relatively soft rubber sleeves. When you inflate the packer, the rubber will enter the fracture and you get an in-print of the fracture intersection with the wellbore. If in the same time you have a compass downhole, you can take a reading and orient the fracture in space. Once at the surface you unwrap the oriented fracture in-print. It is used in hydraulic fracturing applications ) mainly geotechnical and mining).

Hi Manuel - yes, we are currently injecting this into wells as a stimulation technique and have seen very good results. Due to confidentiality, I am unable to share specific well results but can send you sales literature on the nanoparticle fluids if you email me at j.williams@nitrolift.com.

Nitro-Lift Technologies is the master distributor for Nissan Chemical's nanoActiv® Enhanced Flowback Technology (EFT) and Hydrocarbon Recovery Technology (HRT) nanoparticle fluids in the Lower-48.

This revolutionary product is increasing production in wells through surface-modified nanoparticles that actually “lift” and fragment oil in the reservoir allowing it to move the wellbore. The best visual showing nanoActiv®’s ability to dislodge oil in pore space is shown in the following video:

https://youtu.be/DK9Fe_2m__o

OpenGeosys

https://www.opengeosys.org/

Hi, I already looked at it however the paper the doesn't contain the raw data use to build the correlation.

Thanks Appreciate

You can also look at the Chinese Government Scholarship for China University of Petroleum (CUP) in Beijing or Qingdao.

MD will give you the more representative flow length, however the assumption normally that the total well length is contributing to flow. unless there is a PLT survey combined with the pressure build-up, use the total measured depth.

Under typical circumstances, we factor in variations in disolved mineral in log interpretations and ignore variations in most (all?) other reservoir aspects (static modelling and dynamic flow simulation). Occasionnaly, we factor in variations of disolved minerals all over the modelling chain (usually when there is strong variations in inital water salinity). In such cases, it is typical (but not mandatory) to limit mineral presence to the water phase. Changes in mineral content linked to variations of temperature are ignored except in peculiar circunstances such as SAGD. Above comment relate to reservoir development space and time scales not migration or digital rock physics space and time scales. Motivations are obvious considering the physics involved and the limited impact such aspects have on what matters. When to make things more complex and how to improve representation is reasonnably well understood (in my opinion).

From a reservoir / recovery perspective one can consider that infinitely small sized slugs tangent the behavior of simulatenous injection. But this is not true from an operations and design of facilities perspective. Equipments and procedures for true simultanenous operations will differ from those of alternating with short slugs (CAPEX /OPEX will be different, HSE aspects too).

For the second question, there are many possible answers. I would structure things considering different configurations. First configuration would be reinjection of associated gas (minus whatever was used for compression and other utilities) eventually focusing WAG on part of reservoir(s), second configuration would be maximum efficiency level of gas injection (no limit on gas availability).

interested

A few years ago, our company performed a pilot test of immiscible gas injection (in WAG mode) in a mature, waterflooded, light-oil, sandstone reservoir. The injection gas was of high CO2 content and was available as production gas with no sales value from a nearby field.

Conventional EOR method screening criteria anticipated that this was not a case for a very good “match” between EOR process and reservoir/fluid characteristics: immiscible CO2 injection has been successful in steeply dipping reservoirs or in medium-viscosity oils, at relatively high remaining oil saturations.

Our case, in contrast, is a light oil (thus with very small margin for viscosity reduction by the CO2), contained in a horizontal reservoir with a relatively high recovery factor by primary recovery and waterflooding. Besides, permeability is highest at the topo layers and lower towards deeper intervals, a configuration that favors good vertical efficiency by water but not by gas injection.

How was then decided to explore the feasibility of this process in this reservoir? The oilfield to which this reservoir belongs was one of the company’s largest assets at that time; even a small increase in recovery factor meant a very interesting volume of oil. The low remaining oil saturation was considered to have a potential for further reduction with this process, based on very conclusive laboratory tests and pilot – scale simulation studies. Injection gas was readily available for the pilot and another large source of almost pure CO2 was present in a nearby structure and would have been used for commercial expansion. On the other hand, chemical EOR and high-pressure air injection had been considered for this field and discarded for non-technical reasons such as complexity, logistics, costs, operational risk.

We proceeded to the pilot test, after stating very clearly to management that it was a risky process. After about 1,5 years of operation, with 40% pore volume of gas and water injected into two different (separate) pilot patterns, the project was abandoned because the incremental oil was much smaller than predicted and gas production was becoming an operational problem.

Strong gravity segregation of the injected gas was the most likely cause of the poor performance, aggravated by the unfavorable permeability distribution that caused water and gas to follow different flow paths in the reservoir and thus prevented the full recovery mechanism from being effective.

It is all about the shape aberration from the circle, isn't it?

If you can discretize the image to triangular mesh, determine the distance range between the closest and the most distant vertices from the pore barycenter. Calculate weighted average distance between the barycenter and every vertex in that ring. Sum all distances between the neighbouring vertices in the ring and divide with the circumference of weighted mean distance from the barycenter. What you get is a dimensionless positive real number that tells you how many times is the rim of that pore longer than rim of the circle with the radius you calculated as weighted average distance.

If you can't triangulate the image you must work with the intensity values from the image. A circular pore has small area of the transitional intensity gradient in the edge region. A complex pore must have greater area in the same region. If you divide the first one with the second one and take the square root out of it, you should get the ratio analogue to the mesh based solution.

In both solution you should figure out how to compensate for inaccuracy due to discretization.

Industrial grade reservoir simulators like IMEX/ECLIPSE/INTERSECT/... are probably unsuited for the type of work you want to perform. You likely need to access and tweak the physical engine of the simulator. R&D codes are what you are looking for. Some source code, which allows you to tweak physics at core, are open source. Google MRST, ADGPRS, OPENFOAM, etc. I would expect some tweaks to be already available in those codes. This will require some investment on your side into the selected code. It is probably advisable that you select one that is used by people around you.

A very crude alternative is to tweak an industrial grade simulator with restarts or use some of the options that exist in some of those "a little bit far". But this is very crude and messy, I would not recommend that.

I guess a major part of R&D effort in gas flow is directed toward ultra low permeability reservoirs and source rocks. How desorption of gas from kerogen affects flow and EUR.

based on my studies, I realized that when your porous medium is considered to be triangle Unit it will decrease when your pore size decreases ,however when it is circular tube the water pour the biggest pores and move to the smallest one then saturation increases in imbibition. I hope t was related to ur topic :S and dipak can I ask you worked on which equation to describe them mathematically? 

I agree with Andreas.

Usually, carbonates tend to be more oil wet than sandstone. Usually, heavy oil wets more than light oil. Therefore, especially if the reservoir is not carbonate, it is difficult for a super-light liquid such as condensate to actually wet the pore walls.

To answer the question above, I do not consider realistic that the long term occupation of pore space by a condensate in the near wellbore region causes the rock matrix to become more oil/condensate-wet.

Dear Mohsin,

I am a field engineer and managed a number of acidizing operations including acid washing and matrix acidizing. What exactly do you want?

If you have seismic velocities it can be converted into pore pressure in absence of lithology also. the d-exponent curves generated will give us an idea about hydrostatic pressure and deviation from hydro static regime.

You are welcome. You can find more details in 'Reservoir Stimulation' by Michael J.  Economides and Kenneth G. Nolte.

Use. SPIGOT software

Hi Ezzeddine,

The answer would heavily depend on your Fortran skills. The standard for these simulations is the geostatistical software library that comes with Deutch and  Journel, 1992 (reference below). I believe there is a newer edition that includes C code as well. The library is a collection of Fortran subroutines that need to be compiled and includes many different simulation methods (not just turning bands) that may prove useful. There is a matlab program (TBCOSIM) that can create fields using TB.

Finally a similar (fully compiled, thus no programming experience is required) utility is part of the groundwater tools that comes with PEST (www.pesthomepage.org) but it is based on the Sequential Gaussian method and only generates 2D fields.

Hope this helps.

Theo

Deutsch, C., and A. Journel, GSLIB Geostatistical Software Library and User's Guide: Oxford University Press, New York, 340 p, 1992.

Rea-search on Non_Darcy flows in porous media is worth looking into.

Hi Soumitra is very interesting your answer, my question is focused to unconventional plays in petroleum Wells drilled in the MFS Tithonian (Upper Jurassic)

In general, if you intend to use a given simulator in further evaluation of the mechanisms, it is obviously legitimate to use such simulator in the match of the small scale experiment.

The real question I believe you are asking is whether or not a simulator like ECLIPSE 100/300, IX, etc is suitable for representing the large scale phenomenon considering that a wide range of microscopic phenomenon are represented (henceforth potentially oversimplified) by the Kr/Pc input in the model.

There is indeed a wide range of literature on the subject and many have questioned this point. The end result is typically that one need to tailor KrPc to the problem at hand at proposal for specific Kr models. I am not aware of a publication that has convincingly demonstrated one cannot deal with large scale phenomenon using blackoil or compositional simulator, provided the Kr/Pc input is tailored to the problem at hand (or in some case like some of my TOTAL publication relative to multiple oil phases tweaking some other aspect of the simulator). This led to the proposal of a wide range of different Kr models, particularly for 3 phase flow and hysteresis. The CWI injection context you are referring to is only one specific configuration of the KrPc problem, I personally do not see how it is of particular concern compared say to WAG, polymer injection, etc. (I am not a specialist of CWI).

You should concern yourselves about the whole range of flow conditions in field scale simulator when considering the issue.

Hoping this helps. 

Of course it is important.  But the difference in carbonate rocks is that the pore geometry is very much more varied and so the model that you use must include a much wider range of capillary dispersion.  Think only of the effect of secondary porosity.  This range of capillary values throughout the rock will depend on a host of porosity-related factors, ranging from lithology through seminary process to diagenesis and fracturing. This is undoubtedly complex, and is the subject of numerous papers.  Whether you are able to capture this is another question.

Omar

The straight answer for your kind of permeability(0.01 mD) is probably yes, regardless of the type of rock. In principle is a matter of area open to flow hence your horizontal section will have to be very long to achieve some sort of rate that makes economic sense. At this level of permeability there are very few options. I hope it helps

You may also look at the attached SPE Distinguished Lecture about the upgridding and upscalling.

You may want to check out our recent paper on this topic. I can share one or two simulated cases as identified in Table 1 of the attached paper. In that case, I'll need your email address.

Desr Sir, Thanks for the references which involve fluid flow through fractured reservoirs. However, I am specifically interested in securing the details of pressure distribution as a function of space and time in a fractured reservoir. Anyway, thanks for your time. 

for each layer calculate PI from equation 1

then calculate the rate (Q) for each layer from equation 2

See the attached file.

I would suggest you read the experimetal work on "Positive imbibition capillary pressure curves using the centrifuge technique" by M. FLEURY, G. RINGOT and P. POULAIN. I am certainly sure with that, all the issues raised above wil be solved. 

well I'm not in your field. bit my one class in petroleum engineering involved modeling the reservoir as dimensionless. therefore pore volumes injected is really just flow rate used to measure time. for ion concentration we would use the error function to model diffusion effects. I am unable to type the math on my phone but check out multicomponent flow modeling, not sure it you're referencing multiphase flow as well but that is a little more complex. We used Buckley leverett method of modeling injection of water to displace oil. similar concepts in terms of dimensionless dimensions, ie pore volumes injected  (an arbitrary number) replaces time.

Bhaskar

From a heat transfer point of view the three(3) mechanisms (convection......) are the ones driving the process therefore the change in temperature. The answer is yes they would and once you establish a stable system under injection then the "system" (well, near wellbore, casing, cement...) will have different conditions to those when the well is shut-in.  If you take the changes of stresses as a function of temperature as an example then you can see that Young modulus, Heat transfer coefficient and Poisson's ratio will change resulting in different fracture pressures. Most of these mechanical properties are closely linked to the parameters that you mentioned (skin, permeability...) as the way the heat is transferred from the reservoir to the fluid will changes. I attach a publication that I hope helps with your question

Actually a very comprehensive site to answer your questions and much more is at:

Ross Crain put up the site and is the source I have gone to over the last 20 years or so for many issues in oil and gas.

Thanks

A good EOR recipe would take into account the driving forces as well as reservoir characteristics such as wettability. If the reservoir has a good vertical permeability and communication, gravity segregation plays a major role and injecting gas would be a good option. That being said, choice of gas depends on the in situ gas composition as recent studies in the Cantarall field of Mexico suggest the gas composition impacts the Gas-Oil contact movement that you might want to keep track of.

One can also choose a chemical EOR method based on the reservoir wettability and oil characteristics. Depending on the porosity/permeability, surfactants can be chosen as some reservoirs favor production by micro-emulsion formation while others by capillary imbibition (tighter ones).

You need to be specific, as net to gross is used in two specific ways in the industry. Firstly one has the most important; net pay to gross (generally shortened to net to gross), where the amount of effective reservoir (the rock that will contribute to flow etc) is calculated from either logs or core. Effective rock is normally defined by applying a permeability  (often related to porosity via a permeabilty/porosity relationship), volume of shale and water saturation cutoffs. The net pay to gross is of course calculated as the amount of effective reservoir relative to the total interval defined.  The lack of effective reservoir can be the result of many factors including clay/shale in the system, which may be caused by the original sedimentological facies or later clay cementation/diagenesis. It can also be caused by mineral diagenesis such as calcite dolomite and quartz.

The other main use of net gross is to define net reservoir thickness relative to the overall thickness of the interval. Here one is looking at the amount of potential reservoir in the system, such as sand without looking at the effects of diagenesis (for example). This later 'net-to'gross is of course the basis of geological/sedimentological modelling.

A reduction in permeability/effective reservoir and the net to gross does need to be recognised in the development of reservoir models to understand the various permeability pathways in a reservoir. Also if the decrease in net to gross could be related to an increase in interstitial diagenetic clays, whcih in some instances can adversely affect water injection and general flow in the reservoir if care is not taken with conpatibility of injection water

Thank you for yours answer

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.

Hi Rui,

These are the current hot topics

Hydraulic fracturing

Enhanced oil/gas recovery

Smart well engineering

Geertsma approaches reservoir compaction as a poro-elastic problem, i.e. the material is assumed to behave elastically and compacts as the pore pressure drops and the effective vertical stress increases. This requires knowledge of the compressibility of the rock.

This is where usually issues arise, as laboratory tests are generally performed (under in-situ conditions) on samples retrieved from core, which may have undergone some damage by being brought up to the surface, or from coring itself. Small (induced) cracks in the rock could then lead to higher compressibility values measured in the lab.

Though the Geertsma method is a good way to achieve a first approximation on elastic reservoir compaction. However, it does neglect time-dependent mechanisms, which may induce additional compaction, i.e. by grain cracking/failure or by dissolution - the Ekofisk field in the North Sea is perhaps the most telling example of this. Perhaps this is not an issue for your reservoir, but it's good to keep in mind/look into it's possibility.

Concluding if reservoir compaction leads to 'significant' surface subsidence is quite subjective, as it is determined by your definition of significant. Simplest assumption to make is that all reservoir compaction is translated to the surface. It's up to you to determine what is acceptable.

Long story short: first estimates for (elastic) reservoir compaction can be 'fairly easy' obtained, but it might as well be that reservoir deformation becomes a complicated interplay between elastic and plastic deformation... Have a look at 'Subsidence Delay: Field Observations and Analysis' by Hettema et al. (2002). 

Cheers,

Suzanne

We know that - for a geological unit having a significant permeability - with a relatively larger pore throat - permeability is assumed to remain independent of effective pressure.

However, from the discussion; 

" This is because the drawdown is function of the minimum "pore throat" of the fractures " - Does it mean that the fracture permeability is a strong function of effective pressure - where the mean size of the pore throat is relatively smaller?

Then, what happens to the total compressibility; and in turn, hydraulic diffusivity?

Does the diffusivity equation still remain linear (that describes - liquid flow through porous medium)?

Our discussion excludes gas flow through a porous medium (where, the shale gas permeability might vary as a function of effective stress and time).

Thanks for your time and reply.

Suresh Kumar.

This is a fascinating subject, what we know from using Bercovici et al is that considering a two-phase deformation model is; that off course the deformation from any depletion cycle is permanent so we move closer to the critical state line indicating the onset of dilatancy. For shallow and weak reservoirs this transition tends to be very rapid (a few 10th psi maybe) and the loss of metro-physical properties in sandstones might be significant depending on what the original porosity was (Wong T.F. et al 1997). Even if secondary recovery is implemented the deformation has already occurred. The most critical part in our view is whether we reach pore collapse or even grain crushing pressures P*.  I hope it helps, regards

In case where pores become very small, like in some unconventionals, then the phase behavior depends explicitly on the size of pores. More generally speaking, when the size of pores becomes comparable to inter and intramolecular interactions and surface forces, then interaction between rock and fluids can have a large influence on the phase behavior. For instance freezing/melting point of fluids can be very different in nano-pores. A respective method to determine pore size distributions in this way is termed thermoporometry. But for that effect, we talk typically of pore sizes smaller than 1 micrometer or more 100 nm or so.

I would think about a model that captures the deterministic characteristics as well as the random components of the hydrological problem.

thank you for your help and interest that you carried me

Faycal,

first, clarify the following:

what do you mean by ANFIS1 and ANFIS2

Do you use any commercial code/toolbox for fuzzy modeling?

What have you implemented so far?

See this standard : Design, Installation, Repair and Operation of Subsurface Safety Valve Systems. ANSI/API RECOMMENDED PRACTICE 14B

FIFTH EDITION, OCTOBER 2005

Dear Barham,

As stated by Ashkan, the air-brine Pc can be measured in the lab using either the centrifuge or the porous plate technique. In principle MICP and air-brine can be compared by converting the MICP to air-brine using the ration of the IFT between the different fluid pairs. For more details I am attaching two of my earlier publications. I hope that helps you. In both papers I only partly talk about the Pc measurements.

Best Regards

Shehadeh 

I am not familiar with the study you mentioned.  You will never know exactly the residence time of a single molecule.  Some water is apt to circulate faster than others.  But in general the ideas above give you basic approaches that should work, but there are more complex that could come up for specific problem analyses.  If it was important to tie this down closer, or address more holistically, check to see if there were any topographic maps available before the dam was put in.  Of course if there have been major sediment influx or dredging, this might not work.  Get your GIS skills or a friend to develop a Digital Elevation Model from the original topographic data.  Then as suggested, there is variance in water inputs dependent on rain and waters flowing in, evaporation losses, leakage or deep seepage, irrigation or other withdrawals, etc.  But with the GIS and the reservoir data, you probably can identify the conservation pool elevation that they like to maintain (probably impossible in droughts) and the full pool that they do not want to exceed due to flooding of residents, flow over dam, etc.  Both the lake area and mean depth will change with weter level, so you must select at least one as your basic assumption.  But you can also consider flow inputs in a wet year, dry year, average year and then use the low,, high and mean pool depths over dry years, wet years and average years, respectively.  So you can make this really complex if you for some reason wanted to address varied circumstances.  Anyway, the GIS original surface will identify the bottom (or defined by the bathymetry studies) and then you select one or more elevations you want to use for lake surface.  GIS can probably give you mean depths fairly easily if you prefer that based on any water surface elevation of the lake.  The volume of the inflow (stream discharge) in acre feet per year is divided by the capacity assumption of the lake in acre feet, and the result will be years time.  You adjust to similar international units.  So if your reservoir has a mean management pool of 10,000 acre feet of water, and the mean inflow is 1000 acre feet over a normal year, with those assumptions the mean residence time must be 10 years.  But we all know that reservoirs are not uniformly mixed, and under certain conditions due to water temperatures  and where the water releases come from (top, bottom, in-between, some waters are going to circulate faster than others.  There could be wind, lake turnover and other mixing considerations. 

Actually approach on neutron-density curves works very well for a qualitative answer.

Depends what you need it for and if you have some actual core derived measurements over logged interval to calibrate with.

I'll see if I can send you some papers.

You could also try this report which may help you, particularly Appendix C Section C2 which focuses on those drilling technologies that have been shown to deliver better performances than standard conventional drilling methods.

Thanks for your answer 

can you send me your e-mail to this adresse for more details 

This discussion might help.  

Does the case include turbulent flow? (non-Darcy skin factor?). Non-Darcy skin? what flow rates and D are you talking about? 

If he/she is including that, then the number can go high more than normal skin factors due to damage. Also, what are the other conditions: partial penetration, two phase flow ... etc

Here are other good links:

2.http://www.fekete.com/san/webhelp/welltest/webhelp/Content/HTML_Files/Reference_Materials/Skin.htm

3. http://petrowiki.org/Flow_equations_for_gas_and_multiphase_flow

Good luck. 

Thank you Dear Mr. Sallam,

I have see this website it's interesting, but for this articles must be purchased, and I have not a credit card.

Best regards

I think what you are asking is to be found here:

and here

Reza,

As a common practice, for immiscible systems (say WO), the change in capillary pressure for a given rock type is only a function of saturation. This is already accounted for in all simulators by providing a table of Pc as a function of Sw. Another way is to provide an analytical correlation such as Brooks-Corey model.

For miscible systems (say OG), in addition to saturation, the change in Pc is also a function of PVT because sigma (Interfacial tension IFT) may vary. Most simulators can also account for this change using an EOS to calculate sigma from hydrocarbon-phase densities and components composition and Parachor.

Hope this helps

My opinion is No. I agree with Ann. And the same conclusion is true for every model.

If you are testing some feature of a model (let's say production), accuracy in the prediction of that feature does not guarantee accuracy in all it's other features.

Probably the best method for you to test your geostatistical reservoir model (seismic inversion, Acoustic Impedance, Elastic Impedance, Porosity, Lithologies and so on...) is to test it against blind data (because that data is real).

PFLOTRAN (www.pflotran.org) is actively maintained and supported, written in modern Fortran 2003, has an extensive set of capabilities, and runs on machines ranging from laptops to the largest-scale supercomputers.