Page tree

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 7 Next »


Specific data processing and interpretation workflow of multi-well production/injection history and permanent downhole gauges data based on cross-well unit-rate transient responses, usually acquired from multiwell deconvolution.

It has features of both Production Analysis and Well Pressure Testing but since it processes historical data on production rates and permanent downhole gauges and does not require additional activities at well site then from logistical point of view it belongs to the Production Analysis domain.



It includes the following stages:

PDG data filtering
Multiwell deconvolution
Formation pressure history reconstruction (from deconvolution) and NFA Pe forecast (from convolution)
Productivity index history reconstruction (from deconvolution and NFA PI forecast (from convolution)
Interference history reconstruction (from deconvolution) and NFA pressure interference forecast (from convolution)
UTR diffusion modelling

The MRT workflow is following:

  1. Collect flowrates and bottom-hole pressure (BHP) which are normally available with permanent downhole gauges (PDG)
  2. Data filtering
    1. Filter the data for overshoots
    2. Filter the BHP data with wavelet thresholding to reduce the noise
    3. Decimate the BHP data (usually 10:1 or 100:1)
    4. Translate the surface rates to downhole total rate qt with account of BHP at any moment of time
    5. Synchronise total flowrate qt variations with BHP variations
  3. Primary Analysis
    1. Filter out shut-ins and hold drawdowns only
    2. Create material balance (BHP and Pe vs cum Q) and IPR (BHP vs qt) diagnostic metrics
    3. Assess dynamic drainage volumes Ve for all wells – the volumes which well is currently draining with account of interference with other wells
    4. Identify the zones of constant productivity index (PI = const), Steady-states (SS) and pseudo steady-states (PSS)
  4. Deconvolution
  5. Convolution and analysis
    1. Reconstruct formation pressure history
    2. Reconstruct productivity index history
    3. Validate if PI is constant and repeat deconvolution exercises over various time intervals if required
    4. Analyse rates correction and check if it is within the metrological limits and raise allocation concerns and/or advise the corrections
    5. Create unit-rate spider-plot – a pressure impact diagram showing how  one well with unit-rate would be varying the pressure in another well over time
    6. Create historical rates spider-plot – a pressure impact diagram showing how one well was varying the pressure in another well over time
    7. Create historical rates pressure interference map showing a current and cumulative impact from one well on another
    8. Create oil IPR at different formation pressure markups and analyse production optimization potentials 
  6. Analytical modelling 
    1. Perform analytical pressure diffusion modelling of all DTR/CTR using log-derivative log-log plots
    2. Assess potential drainage volumes Ve,max for all wells – the volumes which well would be draining in case it would be the inly producing well in the field
    3. Assess well drainage and cross-well transmissibilities and compare them against each other and against the OH  log interpretation on the map
    4. Analyse additional diffusion model parameters (skin-factor, fracture length, horizontal length, permeability anisotropy) against expectations
  7. Additional studies
    1. Production forecasts
      1. Generate formation pressure and bottom-hole pressure forecasts based on NFA production/injection rates
      2. Generate formation pressure and production forecasts based on constant BHP
      3. Additional forecasts based on various BHP and production scenarios
    2. Numerical pressure tests
      1. Create N2 numerical pressure test scenarios for each DTR and CTR
      2. Check  simulated DTR/CTR against deconvolved DTR/CTR in log-derivative diagnostic plots to understand where exactly numerical model may have discrepancies 
      3. Try various model boundaries, barriers and reservoir properties to improve the match






  • No labels