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Decline Curve Analysis For Unconventional Reservoir Systems — Variable Pressure Drop Case

MS Thesis Defense — Fall 2016 College Station, TX (USA) — 21 October 2016. Decline Curve Analysis For Unconventional Reservoir Systems — Variable Pressure Drop Case. Patrick COLLINS Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA)

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Decline Curve Analysis For Unconventional Reservoir Systems — Variable Pressure Drop Case

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  1. MS Thesis Defense — Fall 2016 College Station, TX (USA) — 21 October 2016 Decline Curve Analysis For Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) +1.214.535.5267 pcollins@tamu.edu MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  2. Presentation Outline Diagnostics • Outline • Brief Biography • Rationale For This Work • Workflow Development • Validation of The Methodology • Application of the Methodology • Summary, Conclusions, and Future Work Calibration Forecasting MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  3. Brief Biography • Born (November 1989) and raised in the Dallas/Fort Worth (DFW) area. • B.S. in Ocean Engineering from Texas A&M University in 2012. • Began pursuing M.S. in Petroleum Engineering in the Fall of 2012. • Began working at DeGolyer and MacNaughton in Spring of 2013 while completing coursework and thesis remotely. • Focus is on reservoir studies and reserve evaluations in worldwide tight and unconventional plays. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  4. Rationale For This Work • Decline curve analysis (DCA) requires constant bottomhole pressure conditions throughout the producing life of the well for meaningful rate extrapolations. • Invalid assumptions bring the applicability of time-rate decline equations into question and methods to accommodate pressure variations are needed. • Data quality, model non-uniqueness/uncertainty, as wells as time constraints often preclude the effective use of model-based production analysis techniques for incorporating flowing pressures. • This work proposes a workflow that combines the convolution principle with recently developed empirical rate decline equations to account for pressure changes during production. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  5. Time-Rate Decline Curve Analysis (DCA): Assumptions • Extrapolation of the best-fit curve through the current or historical production data is an accurate model for future production trends. • There will be no significant changes in current operating conditions or field development that might affect the curve fit and the subsequent extrapolation into the future. • Well production is from an unchanging drainage area with no-flow boundaries (Arps' Assumption) • Major assumption: The well is producing against a constant bottomhole flowing pressure. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  6. Time-Rate DCA: Empirical Model Comparison Transient Flow Rate Match DCA Model Legend: Modified Hyperbolic: HYP Power-law Exponential: PLE Stretched Exponential: SEM Duong: DNG Logistic Growth: LGM Terminal Forecasts • Discussion: • Each decline relation is for a specific flow regime and/or operating condition. • During transient flow many/most models will match data — forecasts vary widely. • Each empirical model implicitly assumes a constant bottomhole flowing pressure. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  7. Time-Rate DCA: Derivative Diagnostic Methods D-parameter: b-parameter: • Discussion: • Derivative trends used to address non-uniqueness in model matches. • Focus is on transient flow behavior. • OBSERVATION of power-law flow derivative trends has led to new DCA relations. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  8. Time-Rate DCA: Variable Pressure • Discussion: • Choke/pressure management is typical for unconventional reservoirs. • Lack of a rate decline signature makes time-rate analysis challenging. • Model-based production analysis (TRP) is more data reliant and time consuming. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  9. Model-based Production Analysis: Theory Constant Rate Solution Variable-RateCase: “Log-log” Diagnostic Plot: Variable-Pressure DropCase: Constant Pressure Solution • Discussion: • Model-based production analysis (TRP) uses a specified well/reservoir model. • Workflow: • Data QC, • Diagnostic calibration (PI/Blasingame Plot), • History matching rate AND pressure data, and • Scenario forecasting. • Superposition is an exact — linearity is required (pseudopressure transform). MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  10. Time-Rate-Pressure DCA: Proposed Methodology "qDb“ Diagnostic Plot: Variable-Pressure DropCase: Constant Pressure Solution Constant Pressure Solution Proxy (PLE model shown): • Discussion: • Proposed methodology: • Empirical decline curve analysis based on the q/Δpfunction. • Apply superposition to yield a variable-pressure rate solution. • Project solution to yield forecast and EUR scenarios. • Methodology steps: • Step 1: Diagnostics • Step 2: Calibration • Step 3: Forecasting MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  11. Workflow Development: Step #1 — Diagnostics Modified Loss-Ratio: Modified Loss-Ratio Derivative: Pseudopressure: Pseudopressure w/k(p): • Discussion: • "STEP 1"(Derivatives) Calculation of the derivative functions (i.e., D(t) and b(t)). • Pseudopressure transformations used for non-linearities (gas and k(p)). • Assume that q(t)/Δp(t) is a proxy for the constant pressure rate model. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  12. t ∫ = dt x 0 Workflow Development: Step #2 — Calibration (Base Rates and Pressures) (Bottomhole Pressure Drop) qD,cpproxy (q/Δp model) • Discussion: • "STEP 2" (Calibration/Superposition) is the convolution (superposition) of variable-pressure drop data using a calibrated pressure drop normalized decline equation. • The calculated D(t) and b(t) parameter trends are used to calibrate model parameters. • Any pressure drop normalized decline expression (model) can be used as a qD,cpproxy (i.e., reservoir model for a well produced at a constant pressure). (Computed Flowrates) MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  13. Workflow Development: Step #2 — Calibration Empirical RelationD(t) Models b(t) Models • Modified-Hyperbolic • Power-Law Exponential • Stretched-Exponential • Duong • Logistic Growth MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  14. Workflow Development: Step #2 — Calibration General Variable Pressure Convolution Equation: Superposition Using the Pressure Drop Normalized Decline Relations: MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  15. Workflow Development: Step #3 — Forecasting • Discussion: • "STEP 3" (Forecasting) is the selection of the future pressure schedule. • Sensitivity cases are generated using prescribed pressure drawdown scenarios. • Validation across a range of diagnostic plots is strongly recommend. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  16. Validation Example #1: Black Oil [Diagnostics] • Discussion: • Black oil scenario was simulated to generate pressure data from near constant rates in order to validate variable pressure decline methodology. • A multi-fractured horizontal well producing from a homogeneous reservoir system was discretized in a numerical commercial flow simulator. • System parameters where chosen to reflect common field values . MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  17. Validation Example #1: Black Oil [Calibration] • Discussion: • Simulated response represents linear flow as expected based on model inputs. • Calibrated diagnostic models adequately capture simulated historical data trends. • Utilization of the convolution integral with the calibrated empirical models and pressure drop data adequately captured the simulated historical rate trend. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  18. Validation Example #1: Black Oil [Forecasting] Forecasting Sensitivities Recovery Results • Discussion: • The final step of the validation process is to impose pressure extrapolation schedules to investigate their effect on the projected oil flowrates. • Additional pressure drawdown clearly resulted in a corresponding period of sustained oil flowrates. • The average thirty year estimated ultimate recovery uplift for all models is ≈ 60% due to increased pressure drawdown of ≈ 2,750 psia. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  19. Validation Example #2: Dry Gas [Diagnostics] • Discussion: • Dry gas scenario aims to introduce additional complexity by introducing compressible flow as a non-linearity . • Pseudopressure transformations are used to satisfy the linearity requirement of superposition. • A multi-fracture horizontal well system was again simulated. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  20. Validation Example #2: Dry Gas [Calibration] • Discussion: • Pseudopressure drop normalized gas flowrate signature indicates linear flow as expected based on inputs. • Each of the models adequately captures diagnostic plotting function trends throughout the history with differences early and late time. • Gas flowrate signature was captured using pseudopressure drop data in the superposition integral. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  21. Validation Example #2: Dry Gas [Forecasting] Forecasting Sensitivities Recovery Results • Discussion: • As with the black oil case, the rate projection is clearly impacted by the pressure assumption. • It is noted here that the forecasts do not incorporate boundary effects or changes in flow regimes (i.e. empirical models). • A ≈61% uplift in thirty year recovery is a substantial response resulting from significant pressure drawdown increase. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  22. Validation Example #3: Dry Gas with k(p)[Diagnostics] • Discussion: • The final validation case attempts to test the workflow for the most complex scenario considered in this work, namely gas production incorporating pressure dependent permeability effects. • Very slight differences in model parameters from the dry gas validation example #2 are noted. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  23. Validation Example #3: Dry Gas with k(p)[Calibration] • Discussion: • A clear power-law diagnostic behavior is noted in the diagnostic plotting functions. • With the exception of the MHYP relation very minimal differences in model behavior throughout time (history and forecast) are noted differing from other examples. • Adequate calibration matches result in a reasonable production match. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  24. Validation Example #3: Dry Gas with k(p)[Forecasting] Forecasting Sensitivities Recovery Results • Discussion: • The last historic flowing pressure along with an extrapolation schedule adding ≈ 2,500 psia in drawdown where used to calculate rate profile sensitivities. • Rate profiles responded as expected with an average thirty year EUR uplift of 36% compared to the 61% uplift for the dry gas validation example #2 . • Less pronounced performance uplift is likely due to the inclusion of the pressure dependent permeability relation within the pseudopressure transformation. • This will be expanded upon as a limitation for Application Examples #2 and #3. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  25. Application Example #1: Shale Oil Well Near Constant pwf Production Data “qDb” Diagnostic Plot (1:2) • Discussion: • First application example represents a shale oil case nearing a constant bottomhole flowing pressure after a year under drawdown control. • The aim of this exercise is to generate forecasting sensitivities to help make a decision on the merits of further bottomhole pressure reduction as a result of installation of artificial lift. • Pressure drop normalized rate data exhibits approximately a half slope indicative of linear flow with D(t) and b(t) trends suggesting power-law behavior. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  26. Application Example #1: Shale Oil Well Near Constant pwf • Discussion: • Each of the models is calibrated to the diagnostic plotting functions while simultaneously calibrating against the flowrate history. • Significant deviation in model behavior is evident as a result of differences in model characteristics . • Slight decline in b(t) trend suggests power-law behavior which is reflected in superior matches for PLE, SEM, and LGM models. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  27. Application Example #1: Shale Oil Well Near Constant pwf Forecasting Sensitivities Recovery Results • Discussion: • It is worthwhile noting that the time-rate-pressure data is highly consistent lending to ease in model calibration. • Noticeable rate response is evident due to additional decrease in bottomhole pressure into the future. • Thirty year EUR increase averages ≈ 25% across all of the models (PLE shown). • The variable pressure decline methodology provides a framework to quickly evaluate economic viability of different productivity enhancement measures. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  28. Application Example #2: Oil Well with Bottomhole Gauge Production Data “qDb” Diagnostic Plot (1:2) • Discussion: • This exercise aims to emphasize the utility of high frequency bottomhole pressure measurements when performing well performance analysis. • The well in question exhibits approximately 75 days of drawdown management resulting in near constant oil flowrates after a period of post stimulation flowback. • Pressure dependent permeability due to in-situ geomechanics is a known phenomena in this formation necessitating pseudopressure transformations. • A clear linear flow trend is identified and used to calculate derivative plotting functions to be used for model calibration. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  29. Application Example #2: Oil Well with Bottomhole Gauge • Discussion: • Reasonable diagnostic matches are obtained for all empirical models. • Final model parameters are typical of those obtained with traditional constant pressure decline curve analysis. • It is clear that the production data is highly consistent and an outstanding history match is obtained across the entire production history. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  30. Application Example #2: Oil Well with Bottomhole Gauge Forecasting Sensitivities Recovery Results • Discussion: • The variable pressure decline methodology provides a great utility in this particular scenario where the flowing bottomhole pressure is still quite a way from reaching a constant value. • Traditional decline curve forecasting assuming a constant bottomhole pressure would be incorrect or misleading at best in this particular scenario. • Utilizing superposition allows for the direct incorporation of additional pressure drawdown which would otherwise require more detailed model based analysis. • A fairly small average production increase of ≈ 23% compared to a large increase in drawdown (3,500 psia) is likely a result of pressure dependent permeability included in the pseudopressure transformation. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  31. Application Example #3: HP/HT Shale Gas Well Production Data “qDb” Diagnostic Plot (1:2) (1:1) • Discussion: • The final single well application example is for a dry gas well producing from a high pressure-high temperature (HP/HT) shale reservoir system. • Significant geomechanical effects causing fracture conductivity loss and/or permeability degredation with pressure necessitate inclusion of k(p) within the pseudopressure transformation to achieve linearity for superposition. • This case is unique from the others due to its steady degredation in productivity over time evident by a marked departure from linear flow and power-law diagnostic trends. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  32. Application Example #3: HP/HT Shale Gas Well • Discussion: • An outstanding match is achieved across all of the diagnostic calibration trends; however it is noted here that non-uniqueness is compounded by additional variables within k(p) expression (i.e.γ and ki) • Differences in empirical model characteristics are clearly observed. • Calibrated model parameters are in line steady productivity loss. Parameter Summary MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  33. Application Example #3: HP/HT Shale Gas Well Forecasting Sensitivities Recovery Results • Discussion: • The forecasting exercise for this example sought to consider four separate final drawdown assumptions for the well in question for each of the five models to try and bracket a range of ultimate recovery values. • Final Δp cases of 6,000 psia, 7,000 psia, 8,000 psia, and 9,000 psia where forecasted. • Very minimal rate response or recovery impact is observed as a result of the various drawdown sensitivities coming as the biggest surprise of all of the cases analyzed. • It was previously hypothesized, and again observed, that the inclusion of pressure dependent permeability when transforming pressures to pseudopressures is significantly impacting the methodologies applicability. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  34. Application Example #3: HP/HT Shale Gas Well Pseudopressure w/k(p): k(p) Expression: • Discussion: • A plot of pressure drop versus pseudopressure drop for the application example in question was created for cases considering no pressure dependent permeability through significant permeability loss with pressure (high γvalues). • It is clear that the inclusion of pressure dependent permeability creates a scenario where significant increases in drawdown translate into insignificant increases in pseudopressure drawdown explaining the lack of impact of forecasting results. • Further linearization measures are likely required for high drawdown cases. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  35. Application Example #4: Multiwell Type Curve Workflow Gas Flowrate vs. Production Time Flowing Pressurevs. Production Time • Discussion: • The aim of this exercise is to demonstrate a workflow for constructing type curves for undeveloped wells honoring diagnostic production signatures and incorporating variable drawdown operating conditions. • An eight well dataset was chosen to illustrate a group of wells where a portion of the wells are flowing against a near constant flowing pressure throughout the production history with the newer wells producing against variable pressure drop conditions. • A series of diagnostic plots aims to identify the characteristic reservoir signature for the well group in question. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  36. Application Example #4: Multiwell Type Curve Workflow Raw Plotting Functions Normalized Plotting Functions MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  37. Application Example #4: Multiwell Type Curve Workflow Parameter Summary • Discussion: • Having identified an appropriate normalization scheme and analogous well group we are now able to calibrate a characteristic empirical profile. • The pressure drop normalized model profile honors the flow regimes observed and serves as a proxy for the constant pressure rate solution in the convolution integral allowing a range of pressure drop sensitivities. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  38. Application Example #4: Multiwell Type Curve Workflow Pressure Extrapolation Senstivities Rate Forecast Type Curves • Discussion: • The final step is to calculate rate profiles according to a range of average pressure drawdown rates. • Clearly more aggressive drawdown management accelerates production while more conservative management results in lower initial rates with shallower decline behavior. • The thirty year recovery results are identical regardless of the drawdown scenario (ultimately same Δp). Recovery Results MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  39. Summary, Conclusions, and Future Work • The conceptual development of a decline curve analysis method-ology for the case of varying wellbore pressures is presented. • Superposition of any "empirical" rate decline relation to generate the rate response and forecast production under pressure constraints is proposed. • Diagnostic (D(t) and b(t) para-meter) plots are used for guidance in determining model parameters. • Prescribed pressure forecasts are used to generate a range of forecast results. • Limitations are observed with increasing system non-linearity notably high drawdown cases exhibiting geomechanical effects. • Methodology is applicable as a first order reservoir surveillance tool (i.e. data consistency verification). • Analytical derivation of empirical models should be persued. • Correlations between model parameters and reservoir parameters need to be investigated. MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

  40. MS Thesis Defense — Fall 2016 College Station, TX (USA) — 21 October 2016 Decline Curve Analysis For Unconventional Reservoir Systems — Variable Pressure Drop Case End of Presentation Patrick COLLINS Department of Petroleum Engineering Texas A&M University College Station, TX 77843-3116 (USA) +1.214.535.5267 pcollins@tamu.edu MS Thesis Defense — Fall 2016 Decline Curve Analysis for Unconventional Reservoir Systems — Variable Pressure Drop Case Patrick COLLINS — Texas A&M University (21 October 2016)

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