What are the deliverables of MRT service ? (e.g.: update to models, interpretation reports, presentation packs, etc)

  • reconstruction of formation pressure and productivity index history
  • quantify the cumulative and current cross-well interference
  • assess transmissibility of the cross-well intervals 
  • predict formation pressure dynamics in various production scenarios
  • reveal wells with the integrity issues


What are the timelines for delivering the results?

  1. Primary data collection is done by Customer and can be normally completed in 1-2 weeks
  2. Each MRT job will be delivered in two months time since the moment of data collection and verification.
  3. The XPM is not part of MRTY and can be delivered as additional service at extra cost and will take additional month

Based on previous projects, what are the estimated uncertainties given for your solution?

The technology is based on pressure changes revival which are caused by rate changes. That’s why uncertainties depend on production history and pressure and rate measurement quality. The more times well rate was changed during production history and the more accurate it was measured the more accurate MRT results will be. Typical uncertainty is 5-10% in formation pressure and 10-20% in cross-well interference and formation transmissibility between the wells.


What are the acceptance criteria given by the previous clients for these type of studies?

The acceptance criteria given by the previous clients for these type of studies are:

  • MRT formation pressure correspondence with traditional pressure transient analysis interpretation 
  • success of production enhancement operations, recommended on base of MRT results

What is the success rate from previous projects?


The current score of success rate is above 90%


Consider a highly compartmentalized field and the wells are interconnected with each other.

In addition, the wells are in commingled production and actual contribution from each zone may not be accurate due to split ratio, zonal isolation issue.   

What is your view on the success rate in doing cross-well deconvolution using production data and SGS data ?

The geological complexity and commingled production is not related to deconvolution process and its success.

The key requirement for deconvolution success is the eventful rate variation history during the time when PDG was installed and quality of the rate hisotry and pressure history data (resolution, contamination and frequency of data sampling).

MRT will be able to decipher only those wells and intervals where the rate variations were numerous and substantial.

In most practical cases in Malaysia we have manages to recover 90 % of the intervals and I don't remember even a single case when a group of wells was totally uninformative.

Once PDG is deciphered in terms of reference transient pressure response we use 2D-grid or 3D-grid numerical express tests  to match them. 

These exercises are performed under a supervision of  Client's GG/PP/RE in order to ensure that we test realistic scenarios in reservoir continuity and layering. 

Pls, note again that deconvolved transient responses usually do NOT represent unambiguous scenario on geological structure and layering. But they do represent a substantial constraint on the possible geological scenarios/layering and help GG/PP/RE team understand reservoir and well behaviour better.  

This workflow is very similar to conventional PTA workflow using the diagnostic log-log plots and calibrating the 2D/3D model on this data.

The end results of the MRT study are very straightforward for embedding into a final 3D model which is usually done by our Clients in-house. 


How can MRT be blind tested to ensure its efficiency in pressure prediction and reservoir characterizations ?

There are two major testing/verification techniques for MRT in general and Multiwell Deconvolution (MDCV) in particular.

One of them is actually a field test which is fast and easy to perform both for Sofoil and Client and does not need any help from Nafta College .

The other one is a lengthy synthetic test which Nafta College normally performs for Sofoil to test their progress in MDCV (and btw Self-Pulse Testing  and Cross-well Pulse-Code Testing and 2D/3D numerical) algorithms.

For 100% assurance in technology capabilities both tests should be performed. 

But synthetic test provides more accurate results and most importantly can test more MCDV features comparing to the filed test.

1. Field Test

After  Sofoil completes some MRT study Client continues recording PDG and production history for few months while Sofoil does not have access to this data.

1.1. Client hands over the production history data to Sofoil 

1.2. Sofoil will use previously deconvolved transient responses to convolve them with the new production history to predict PDG data as response to the rates variations and Pe forecast and hand this over to Client

1.3. Client will easily verify the accuracy of the PDG prediction with MDCV predictions and if some-shut-ins have luckily occurred during this period of time then Client team will perform conventional PTA and assess Pe at this moment of time and compare it to MDCV predictions. 

This test is very simple and fast but it only tests MDCV engine (not the whole MRT service) and it only tests one component of MDCV engine: bottom-hole and formation pressure prediction as response to multi-well production.

Client will not be able to understand how good is MDCV in assessing the reservoir properties round and in between the wells because you do not test deconvolved transient responses separately.

2. Synthethic Test

2.1. Nafta College creates synthetic 3D field (same as it does in PetroCup sessions) similar to Client's field (in case you are not comfortable to hand us over the actual 3D model or at least a test sector) and generate high level report on geometrically averaged near-well and cross-well parameters (skin-factor, transmissibility, pressure diffusivity and drainage volume).  

Normally three wells in NDR field: PDG well and two producers around at different distances and in different geological compartments. 

Nafta College will anyway invade original model severely to build super-fine LGR to reproduce the pressure transient specifics which are otherwise lost in conventional full-field models.

2.2.  Nafta College borrows production history from Clients field and generates PDG response in selected well and hand over the synthesized PDG data + flow rate history to Sofoil.

2.3. Sofoil provides a short version of MRT report with a  focus on MDCV and compares the results to the geometrical average of 3D properties to demonstrate the tolerance at which MRT asses the non-uniform geological data. 

The deconvolved transient responses will be also delivered in the report (and as data files).

2.4. Nafta College creates N^2 = 9 transient responses in super-fine 3D-grid dynamic model: 1 —> 1, 1—>  2, 1 —> 3,   2 —> 1, 2 —>  2,  2 —> 3,   3 —> 1, 3 —>  2,  3 —> 3 and provides the conventional PTA interpretation for 3 single-wells self-responses 1 —> 1,   2 —>  2,  3 —> 3 and conventional PIT interpretation for 6 cross-well responses: 1—>  2, 1 —> 3,   2 —> 1,2 —> 3,   3 —> 1, 3 —>  2 and hands over a brief report and hard data files.

2.5. Nafta College provides comparison of MRT results against conventional  PTA/PIT and against 3D-averaged reservoir properties and generate brief report and hands all the data to Client. 

In a perfect scenario the results of MRT and PTA/PIT will closely match (like 10-15% depending on the outer wells contamination) and more or less match 3D-average values, depending of the homogeneity of the reservoir). 

In homogeneuous 3D model without outer wells contamination all three technique will perfectly match within < 5 % accuracy.

This is just first part of the workflow and Client needs to repeat the same exercise in-house for a fair blind verification.

2.6. My advise is for Client to collaborate with some Academic Group  (for example, a University) to study the Test Report and Data from Nafta College and Sofoil , understand the logic of the test, modify the original 3D model and production history, repeat Nafta exercises in-house and hand new production histories and new PDG data readings over to Sofoil

2.7. Sofoil provides a short version of MRT report with a focus on MDCV and compares the results to the geometrical average of 3D properties to demonstrate the tolerance at which MRT asses the non-uniform geological data. 

The deconvolved transient responses will be also delivered in the report as hard data files.

2.8. Client in collaborate with Academic Group will compare the predictions and decide on validity of the test and generate primary ideas of the scope of MRT applicability.


Any experience in doing interpretation for gas injection projects, and worse, in a water-alternating-gas (WAG) injection projects. Our worries is that gas is a very compressible fluid and will dampened the pressure responses to a noise-level and negates the MRT applications.  

Nope, MRT was never done with WAG but we are sure it is applicable. The gas rim can not dampen pressure response to rate variation. 


Use of surface pressure gauges data (continuous digital data) for both WAG injectors and oil producers, instead of downhole gauges. Reasons is that all our injectors and producers are equipped with electronic gauges but less / fewer wells do have downhole pressure gauges (though some wells have multiple gauges at zonal level).

The THP gauge in WAG injector may work but only if installed at downstream part of the wellhead's choke. 

The THP gauge in an oil well will hardly contain any valuable information on reservoir connectivity due to a very high damping factor of the gasified oil column and instability of the gaslift flow in the tubing.


How much reservoir physics involvement in the deconvolution exercise that forms basis for reservoir connectivity

None. The deconvolution itself only correlates rates and BHP. 
But when Customers call for XPM service (cross-well pressure modelling) we use numerical unstructured Voronoj grid simulations to match deconvolved data and in this case we need a complete set of input data -- same as for 3D modelling.


Applicability of MRT for a multiple-stacked reservoirs where rates are measured on a well-level instead of reservoir level.

The MRT is still applicable and valuable. Some outputs will be solid: like PI history,  cross-bed average formation pressure history, cross-well connectivities etc.

The question is to how to give the MRT results a bed-by-bed meaning. This will require a PLT profile to complete the picture. 
Or we can at least assume a flow profile from OH data -- this is what simulation engineers do everyday when they don't have PLT.


Understanding bit more on the eventual computed parameter, that is the Reservoir Connectivity – is it a fixed parameter (function of reservoir properties) or a variable / a changing parameters - depending on the production –injection performance at a given time?

Reservoir connectivity is a bit of misnomer unless it is used to define a category of the actuall cross-well connectivity properties.

The actual cross-well connectivity properties are:

1. CTR = unit-rate cross-well transient response – this is an ultimate form of cross-well connectivity information and all others can be derived from it.

2. Cumulative impact  = a number showing how much pressure was drained (or gained) due to production from a given offset well for the whole period of tis work. This number is affected by production history of the offset well.

3. Current impact  = a number showing how much pressure is currently drained (or gained) for the last month of production from a given offset well. This number depends on the current flowrate in offset well. 

4. Normalized current impact  =  a number showing how much pressure is currently drained (or gained) for the last month of production from a given offset well as it was producing with unit-rate. This number is independent on the flowrate history of the offset well but still depends on the well trajectory, well-reservoir contact geometry and properties, distance between the wells and reservoir geometry.

5. Cross-well transmissibility = a number characterising the cross-well average reservoir property between the wells. It's simply a permeability-thickness product divided by fluid viscosity.


Experience on how do you use this reservoir connectivity mapping on reservoir surveillance and management of the field

This is what we call MRT applications in cross-well connectivity area.

The MRT can signal on

  • poor drainage volume due to compartmentalization
  • poor flowing thickness due to extended shale breaks
  • thief injection/production suspects

But by far the most complete usage of MRT outputs is to include them into cross-well pressure modelling (XPM) exercise.


When performing automatic history matching, what are possible models for Pn(t) that MRT will consider, say, radial flow, homogeneous/heterogeneous, single/multiple phase, fault/no fault, what kind of boundary?

In a producing field, the pressure in a well is affected by its own production rate, as well as the production or injection rates of surrounding wells–a process known as superposition. MRT analysis aims to invert the superposition affected signal into its individual components, based upon deconvolution analysis. Deconvolution is a mathematical procedure which extracts unit-rate Drawdown Transient Responses (DTR's) and Cross-well Transient Responses (CTR's) from a group (or cell) of wells in a producing field. A DTR is extracted for the well containing a downhole pressure gauge (PDG well or focus well), while CTR's are extracted between the PDG well and the other offset wells having rate-only data.

DTR is equivalent to a long-term Drawdown Survey in the focus well, as if none of the offset wells was working for the whole period of production history. CTR is equivalent to a long-term step-wise Pressure Interference Test between two wells as if the responding well is shut-in and other remaining wells were not working.

Once the DTR and CTR's have been extracted from the superposed signal by deconvolution analysis, then they can be interpreted using  conventional well test analysis of pressure diffusion. They are usually analyzed on a log-log plot of pressure and pressure derivative. The shapes of the DTR and CTR's provide indications of the well-reservoir contact geometry, flow geometry and boundary type. The MRT does not imply limitations to the possible shape of DTR/CTR and as such is capable to capture all possible types of pressure diffusion phenomena, depending upon the actual reservoir characteristics.


Is there any real pressure data need to be retrieve during the study execution?

Yes, MRT requires long term bottom-hole pressure and production/injection data

Is there any pressure gauges need to be install Or just surface monitoring system will be install from your side. Is it enough the current pressure gauges installed with artificial lifting system

Bottom-hole pressure at artificial lifting system can be used for MRT. Surface TPH can be used in injectors


Send the cleared procedure and plan starting from collecting data till complete the study.

procedure plan 


According to your required data for the project, we just ask why we need that all data. As we understand that the study and recommendation will be based on pressure data.

Long term bottom-hole pressure and production/injection data are required for multiwell deconvolution additional date required for comprehensive analysis


If all data for the well required, So what is the different between WRM team review and MRT? What sort of recommendation will be implemented what cannot raised by the team?

MRT provides additional information that cannot be revealed by any other technology, presented on the next slide

  • Quantitative interference between wells
  • Presence of geological boundaries and for production optimization
  • Formation pressure dynamic
  • Productivity index dynamic
  • Well skin-factor dynamic
  • Interference free well drainage area and reserves
  • Formation transmissibility (kh/) -> permeability
  • Quantitative history of aquifer support