Final Presentation College Station, TX (USA) — 11 August 2015 Chulhwan SONG Department of Petroleum Engineering Texas A&M University College Station, TX (USA) APPLICATION OF NUMERICAL MODEL-BASED DYNAMIC FLOW ANALYSIS ON A GAS FIELD WITH COMPLEX BOUNDARY CONDITIONS

Outline ● Purpose of the Study: ■ Apply modern model-based PA and PTA to a gas field case. ■ Characterize complex properties and geometries of reservoir using numerical composite model. ● Statement of the Problem: ■ Variable operating conditions. Insufficient spatial information. ■ Operating issues: condensate banking, edge-water influx. ● Model-Based Production Analyses: ■ Analytical circular reservoir model-based PA for determination of average reservoir properties and GIIP. ■ Gas material balance analysis for GIIP comparison and drive mechanism verification. ● Model-Based Pressure Transient Analysis: ■ Numerical composite model-based PTA according to close interpretation of pressure derivative response. ■ Diagnosis of reservoir properties, and analysis of condensate banking zone and edge-water influx. ● Summary & Conclusions: ■ Summary of the work done. Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 Slide — 2/34

Purpose of the Study ● Our Primary Objectives: ■ Present a workflow for modern dynamic flow analyses. ■ Present applicability of model-based PA and PTA to accurately diagnose reservoir properties and boundary characteristics in a gas field. ■ Demonstrate how to build numerical composite model corresponding pressure derivative responses during the model-based PTA ■ Investigate characteristics of condensate banking zone and aquifer, which are major operating issue of target field Slide — 3/34 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015

Statement of the Problem Slide — 4/34 ● Reasons why modern dynamic flow analysis is necessary: ■ Conventional rate-time analysis is not applicable (due to variable operating conditions of this field). ■ Limited spatial information about reservoir boundary: a measured dynamic data set from a well is the only data source for analysis ■ Possibility of condensate banking issue ■ Possibility of edge-water drive in B5 Layer ■ Good pressure data quality (measured by permanent bottomhole gauge) Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 Reservoir structure map of DH-1 field

Model-Based Production Analysis (PA) Final Presentation College Station, TX (USA) — 11 August 2015 Chulhwan SONG Department of Petroleum Engineering Texas A&M University College Station, TX (USA)

Slide — 6/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Model-Based Production Analysis Concepts: ■ Diagnostic Plots — "Blasingame Plot" — Pseudopressure-normalized rate functions — "log-log Plot" — Rate-normalized pseudopressure functions — Plotted against material balance time in log-log scale ■ Procedure — Data loading and editing — Extraction of flow period of interest. — Model generation and refinement using diagnostic plots — Forecast and sensitivity study.

Slide — 7/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Synchronize all rate and pressure data ● Refine the production history (i.e. remove obvious error data) Data Loading & Editing Model Generation & Refinement Sensitivity Study / Forecast ● Extract interval(s) of time on which PA will be performed ● Diagnostic tools ■ Blasingame Plot ■ log-log Plot ■ History Plot Objectives: ● To obtain a match between the models and the real data in all the diagnostic plots ● Known model parameters and well configurations are imposed ● Unknown parameters should be adjusted ■ By manually, with trial and error ■ By using non-linear regression ● Assess the sensitivity of each parameter ● Forecast the future production with producing pressure scenario ● Recommendations for future operations ● Detailed Procedure:

Slide — 8/34 Model-Based Production Analysis ● Production Analysis Concepts: ■ Blasingame plot ■ log-log plot Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015

Slide — 9/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Well-1P ■ Diagnostic Discussion — All diagnostic plots well-matched — Average permeability can be estimated by level of early 10 days, t e — Pseudo-steady flow regime is shown in late time.

Slide — 10/34 Model-Based Production Analysis ParametersWell-1PWell-2PWell-3PWell-4PUnit Model typeAnalytical Well modelVertical Reservoir modelHomogenous Boundary modelCircle pipi psia Skin factor dimensionles s Permeability mD R e (no flow) ft GIIP BSCF Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Summary of Model Parameters for Each Well

Slide — 11/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Well-2P ■ Diagnostic Discussion — All diagnostic plots well-matched

Slide — 12/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Well-3P ■ Diagnostic Discussion — All diagnostic plots well-matched

Slide — 13/34 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Well-4P ■ Diagnostic Discussion — History plots are NOT well-matched in late production days — Need further analysis on reservoir boundary

Slide — 14/34 Gas Material Balance Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Gas Material Balance Analysis for Wells-1P, 2P, and 3P ■ Objectives — GIIP comparison for verification of the previous model-based PA — Identification of drive mechanism (especially targeting well-4P) ■ Discussions — A linear relationship between the values of p/Z vs G p can be confirmed by the straight trend line — Pressure support from a region outside the reservoir may be very small or negligible for Wells-1P, -2P and -3P

Slide — 15/34 Gas Material Balance Analysis Well (in Layer) GIIP estimated by model-based PA (BSCF) GIIP estimated by gas material balance (BSCF) well-1P (in B2 Layer) well-2P (in B3 & B4 Layers) well-3P (in B3 & B4 Layers) well-4P (in B5 Layer) Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Gas Material Balance Analysis for Well-4P ■ Discussions — Deviation from straight line: suspected case of aquifer affecting reservoir. — GIIP Difference is significant compared to other wells. — Need further analysis

Model-Based Pressure Transient Analysis (PTA) Final Presentation College Station, TX (USA) — 11 August 2015 Chulhwan SONG Department of Petroleum Engineering Texas A&M University College Station, TX (USA)

Slide — 17/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Model-Based Pressure Transient Analysis Concepts: ■ Representative Diagnostic Plot — Log-log plot ■ Pseudopressure-drop ■ Bourdet pseudopressure-drop derivative ■ Plotted against shut-in time in log-log scale ■ Magnify pressure responses ■ Procedure : basically similar with previous model-based PA — Data loading & editing — Extraction of pressure buildup period(s) of interest. — Model generation and refinement using diagnostic plots — Forecast and sensitivity study.

Slide — 18/34 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 Model-Based Pressure Transient Analysis ● Well-1P ■ Three Representative Buildups were selected according to: — Relatively stable production rate for a sufficient time just before start of shut-in. — Relatively long shut-in time ■ Superimpose buildups in a log- log plot — Provides good insights about pressure response from early to late time ■ Bourdet pseudopressure derivative distinguishes different flow regimes as time passes.

Slide — 19/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Five Different Flow Regimes of Well-1P

Slide — 20/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Construction of 2D Geometry Map for Numerical Composite Model ■ Five Regions Closely corresponding the pressure derivative trends — Only outer boundary was based on given structure map. — Shape and properties of each zone were tested through extensive trial and error

Slide — 21/34 Model-Based Pressure Transient Analysis ParametersWell-1PUnit Model typeNumerical C2.4bbl/psi Skin factor time-dependent (7.5 ~ 11) dimensionless pipi 3550psia k68mD Reservoir modelHomogeneous Composite zone 1 Mobility ratio (M)4dimensionless Diffusivity ratio (D)4dimensionless Leakage factor (α)1dimensionless Composite zone 2 Mobility ratio (M)600dimensionless Diffusivity ratio (D)600dimensionless Leakage factor (α)0.06dimensionless Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Discussions of Matched Model ■ Extent of reservoir boundary estimated ■ First composite zone (red) — Condensate banking zone has 4 times smaller mobility and diffusivity ■ Second composite zone (yellow) — Very weak but continuous pressure support to the reservoir

Slide — 22/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Diagnostic Plots ■ The model provides satisfying matching results on all buildups in each of diagnostic plot

Slide — 23/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Sensitivity study ■ As Average Reservoir Permeability Decreases, — Vertical level of pseudopressure drop functions go up — Effect of boundary appears later — In very small permeability (e.g. k=10 mD, green), signal from boundary significantly delayed, and does NOT appear until the end of investigation.

Slide — 24/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Sensitivity studies of composite zones ■ As Mobility and Diffusivity of Condensate banking zone become worse, — Vertical level goes up, and duration of the transition interval increases. ■ As Mobility and Diffusivity of Outer Composite zone increases, — More active pressure influx to the reservoir is expected — Effect of beyond-boundary region appears earlier

Slide — 25/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Well-4P ■ Three Representative Buildups were selected ■ Superimpose buildups in a log-log plot ■ Three flow regimes appear in Bourdet pseudopressure derivative — Short IARF — Long boundary dominated flow regime

Slide — 26/34 Model-Based Pressure Transient Analysis ParametersWell-4PUnit Model typeNumerical C0.2bbl/psi Skin factorTime dependentdimensionless pipi 3600psia k90mD Reservoir modelHomogeneous Composite zone 1 Mobility ratio (M)16dimensionless Diffusivity ratio (D)8dimensionless Leakage factor (α)1dimensionless Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Numerical Composite Model for Well-4P ■ Extensive trial and error for model refinement ■ Extent of reservoir boundary estimated ■ Average reservoir properties estimated ■ Composite zone beyond reservoir boundary(yellow) — pressure support.

Slide — 27/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching on Diagnostic Plot of Well-4P ■ Good matching until 80 days ■ NOT acceptable matching after 80 days — Bourdet derivative trend deviates from straight line of data in very late time — Additional pressure support to the reservoir can be considered in very late time

Slide — 28/34 Model-Based Pressure Transient Analysis ParametersWell-4PUnit Model typeNumerical C0.2bbl/psi Skin factor time-dependent (1.5~6) dimensionless pipi 3600psia k90mD Reservoir modelHomogeneous Composite zone 1 Mobility ratio (M)10dimensionless Diffusivity ratio (D)2.8dimensionless Leakage factor (α)0.05dimensionless Aquifer modelCarter-Tracy φ0.01dimensionless k0.9mD riri 7500ft rere 12000ft Thickness of aquifer12.5ft Encroachment angle180 ° Total compressibility3.00E-06psi -1 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Numerical Composite Model with Aquifer Model ■ An aquifer model was added in northern outer boundary of B5 Layer ■ Extensive trial and error ■ Very weak aquifer was modeled to explain the pressure influx in very late time

Slide — 29/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results of Diagnostic Plots ■ The numerical aquifer model finally provides satisfying matching results on all buildups in each of diagnostic plot

Slide — 30/34 Model-Based Pressure Transient Analysis Buildup Starting time of shut-in (Days) Duration of shut-in (Days) Skin factor (dimensionless) Buildup Buildup Buildup Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Matching Results ■ Also good matching on pressure history plot ■ Skin factors of buildups increase from 2.3 to 6 as time passes — suspected case of growing condensate bank

Slide — 31/34 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Application of the Numerical Aquifer Model of PTA to PA ■ Improved History Matching — The numerical aquifer model nicely explains the trend of production history Previous history match with analytical circular closed boundary model History match with numerical aquifer model

Final Presentation College Station, TX (USA) — 11 August 2015 Chulhwan SONG Department of Petroleum Engineering Texas A&M University College Station, TX (USA) Summary and Conclusions

● Summary: ■ Performed analytical model-based production analysis (PA). ■ Performed gas material balance analysis to confirm the PA and to investigate drive mechanism. ■ Performed numerical model-based pressure transient analysis (PTA). ■ Presented a workflow of modern model-based dynamic flow analysis (including PA and PTA). ● Conclusions: ■ PA with analytical circular reservoir model successfully diagnoses average reservoir properties and GIIP for all wells except for Well-4P. ■ Gas material balance analysis confirms GIIP of previous PA in Well- 1P, -2P and -3P, and raises suspicion of aquifer in Well-4P. ■ PTA with numerical composite model enables flexible analysis of complex reservoir geometry and spatial discontinuity of reservoir. — Close investigation of flow regimes in Bourdet pressure derivative trend is essential to build proper model. — A condensate banking zone and a weak aquifer beyond outer boundary were successfully modeled, and showed good matching results. Slide — 33/34 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015

Final Presentation College Station, TX (USA) — 11 August 2015 Chulhwan SONG Department of Petroleum Engineering Texas A&M University College Station, TX (USA) APPLICATION OF NUMERICAL MODEL-BASED DYNAMIC FLOW ANALYSIS ON A GAS FIELD WITH COMPLEX BOUNDARY CONDITIONS