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Specific type of Production Analysis (PA) workflow based on correlation between multi-well production/injection history and bottomhole pressure history from permanent downhole gauges (PDG) data records.

The key simulation engine of MRT is Pressure Convolution which is based on Unit-rate Transient Responses (UTR) retrieved from Production rates / PDG data history by means of Pressure Deconvolution.

It does not require new data acquisition at well site and makes use of historical dynamic data records, usually few months or longer.


Motivation

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Production rate in producing well depends on its productivity index 

LaTeX Math Inline
bodyJ
, current formation pressure 
LaTeX Math Inline
bodyp_e
 and current BHP 
LaTeX Math Inline
body--uriencoded--p_%7Bwf%7D
:

LaTeX Math Block
anchorqup
alignmentleft
q_1^{\uparrow}(t)=J \cdot \left( p_e(t) - p_{wf}(t) \right)

and as such depends on completion/lift settings (defining 

LaTeX Math Inline
body--uriencoded--p_%7Bwf%7D(t)
) and how formation pressure is maintained 
LaTeX Math Inline
bodyp_e = p_e(t)
 over time.

It keeps declining due to the offtakes:

LaTeX Math Block
anchorpeup
alignmentleft
p_e(t) = p_e[q_1^{\uparrow}(t), q_2^{\uparrow}(t), q_3^{\uparrow}(t), \dots]

and maintained by either aquifer or Fluid Injection and in the latter case depends on injection rates:

LaTeX Math Block
anchorpedown
alignmentleft
p_e(t) = p_e[q_1^{\downarrow}(t),q_2^{\downarrow}(t),q_3^{\downarrow}(t),\dots ]

The combination of 

LaTeX Math Block Reference
anchorqup
LaTeX Math Block Reference
anchorpeup
 and 
LaTeX Math Block Reference
anchorpedown
 lead to the correlation between production rates, injection rates and bottomhole pressure variation.


The ultimate purpose of MRT is to extract maximum information from correlation between the long-term (few months or longer) flowrate history and BHP history (recorded by PDG).

It is essentially based on the fact that BHP in a given well (whether producing or injecting) responds to flowrate variation in the same well and may (or may not) respond to flowrate variation in offset wells.

This information is further related to well flow performance and cross-well connectivity.

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  • Create short-term prediction model on production response to various multi-well production regimes

  • Compare the well dynamics and and cross-well connectivity with expectations and identify the candidates for drilling, workover or additional well surveillance

  • Assess dynamic reservoir properties

Inputs

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Drilled formation pressure

LaTeX Math Inline
bodyp_d
 from DST – Drill Stem Test

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Outputs

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Production History



Simulated total subsurface flowrate history,

LaTeX Math Inline
bodyq_t(t)

Simulated BHP history,

LaTeX Math Inline
body--uriencoded--p_%7Bwf%7D(t)

Simulated formation pressure history,

LaTeX Math Inline
bodyp_e(t)

Simulated Productivity Index history,

LaTeX Math Inline
bodyJ_t(t)

Simulated Cross-well interference history

LaTeX Math Inline
body--uriencoded--p_%7Bk \rightarrow m%7D(t)

Production Forecast

Rate forecast under Pressure Control regime, 

LaTeX Math Inline
body--uriencoded--p_k(t), \%7B q_m(t) \%7D \rightarrow q_k(t)

BHP forecast under Liquid Control regime, 

LaTeX Math Inline
body--uriencoded--\%7B q_m(t) \%7D \rightarrow p_%7Bwf, \, k%7D(t)

Formation pressure forecast under Liquid Control regime, 

LaTeX Math Inline
body--uriencoded--\%7B q_m(t) \%7D \rightarrow p_%7Be, \, k%7D(t)

Diagnostic Metrics









Cross-well interference map
Unit-rate Transient Response Matrix (UTRM)
Unit-rate Transient Response Spider (UTRS)
Material Balance Pressure Plot
Inflow Performance Relationship (IPR)
Cumulative Productivity Plot (Hall Plot)
J-plots
WOR diagnostics
GOR diagnostics
Primary Well & Reservoir properties


Potential drainage volume
Current dynamic drainage volume
Secondary Well & Reservoir properties




Apparent transmissibility
Apparent skin-factor 
Fracture half-length
Dynamic fracture pressure threshold


Inputs

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Applications

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Production forecasts


Predict formation pressure without shutting wells down and avoiding production deferment
Short-term production forecasts for different multi-well production scenarios
Selecting well-intervention candidates




Identify well-intervention candidates with possible thief production/injection
Identify well-intervention candidates with possibly inefficient reservoir flow profile
Identifywell-intervention candidates for Rate Optimization
Identifywell-intervention candidates for producer ↔ injector conversion
Dynamic Model Calibration




Adjusting historical production allocation
Adjusting the potential reservoir volume extension at different directions
Adjusting faults / channels / compartmentalization
Adjusting fracture model

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