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: ■ Unstable 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 ■ Non-uniqueness of the model-based PA ■ Gas material balance analysis for GIIP comparison and drive mechanism verification. ● Model-Based Pressure Transient Analysis: ■ Numerical composite model-based PTA based on 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 — 2/30

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 — 3/30 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015

Statement of the Problem Slide — 4/30 ● Reasons why modern dynamic flow analysis is necessary: ■ Conventional rate-time analysis is not applicable (due to non-constant operating conditions of this field). ■ Limited spatial information about reservoir boundary: measured dynamic data are 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 — 6/30 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 — “Loglog Plot” — Rate-normalized pseudopressure functions — Plotted against material balance time (t e ) in loglog scale ■ Procedure — Data loading & editing — Extraction of flow period of interest. — Model generation and refinement using diagnostic plots — Forecast and sensitivity study.

Slide — 7/30 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  Loglog 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 errors  By using non-linear regressions  Assess the sensitivity of each parameter  Forecast the future production with producing pressure scenario  Recommendations for future operations ● Detailed Procedure:

Slide — 8/30 Model-Based Production Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Production Analysis Concepts: ■ Blasingame plot ■ Loglog plot

Slide — 9/30 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 — Avg. k can be estimated by level of early 100 days, t e — Pseudo-steady flow regime is shown in late time.

Slide — 10/30 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 — 11/30 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 — 12/30 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 — 13/30 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 — 14/30 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 — 15/30 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 — 17/30 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: ■ Diagnostic Plots — “Loglog Plot” — Pseudopressure-drop — Bourdet pseudopressure-drop derivative — Plotted against shut-in time in loglog scale — “Semilog Plot” — Pseudopressure function — Plotted against superposition time ■ 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 — 18/30 Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 Model-Based Pressure Transient Analysis ● Field Case #2 ■ PLE Relation — We focus on data > 20 days. — Power law D(t) and b(t) character. — Excellent q g (t) match. ■ Match Parameters — q gi = 1715 MSCFD — Ď i = — n = 0.45 — D ∞ = 0 (default). ■ EUR — 1.63 BSCF

Slide — 19/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015

Slide — 20/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 21/30 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 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 22/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 23/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 24/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 25/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days. — Power law D(t) and b(t) character. — Excellent q g (t) match. ■ Match Parameters — q gi = 1715 MSCFD — Ď i = — n = 0.45 — D ∞ = 0 (default). ■ EUR — 1.63 BSCF

Slide — 26/30 Model-Based Pressure Transient Analysis ParametersWell-4PUnit Model typeNumerical C0.2bbl/psi Skin factor4dimensionless 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 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 27/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 28/30 Model-Based Pressure Transient Analysis ParametersWell-4PUnit Model typeNumerical C0.2bbl/psi Skin factor time-dependent (1.5~6) dimemsionless pipi 3600psia k90mD Reservoir modelHomogeneous Composite zone 1 Mobility ratio (M)10dimemsionless Diffusivity ratio (D)2.8dimemsionless Leakage factor (α)0.05dimemsionless 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 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 29/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 30/30 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 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

Slide — 31/30 Model-Based Pressure Transient Analysis Final Presentation — Chulhwan SONG — Texas A&M University College Station, TX (USA) — 11 August 2015 ● Field Case #2 ■ PLE Relation — We focus on data > 20 days.

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 independent production data and rate-time analyses. ■ Integrated the two analyses with an iterative correlation scheme. ■ Discussed challenges in unconventional well performance analysis. ■ Presented a workflow that attempts to reduce non-uniqueness. ■ Introduced PTA as an analysis tool in unconventional reservoirs. ● Conclusions: ■ From this work we conclude the following: — Rate-time diagnostics exhibit primarily hyperbolic decline character for our 55-well data set. — PLE relation produces the most conservative EUR estimates. — Bilinear flow (1/4 slope) is the predominant flow regime. — Linear flow (1/2 slope) is the exclusive PTA diagnostic. — Correlation scheme using a "tuning" technique improved the EUR relationship between model-based and rate-time analyses. — Model-based production analysis is an effective tool for cases of erratic production history, while rate-time analysis requires smooth, lightly-interrupted flow periods. Slide — 33/30 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