Well-Performance Study Integrates Empirical Time/Rate and Time/Rate/Pressure Analysis

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The purpose of the complete paper is to create a performance-based reservoir characterization by use of production data (measured rates and pressures) from a selected gas-condensate region within the Eagle Ford Shale. The authors use modern time/rate (decline-curve) analysis and time/rate/pressure (model-based) analysis methods to analyze, interpret, and diagnose gas-condensate well-production data. Reservoir and completion properties are estimated; these results are then correlated with known completion variables. The time/rate and time/rate/pressure analyses are used to forecast future production and to estimate ultimate recovery.

Production-Analysis Work Flow

The data required for the completion of the proposed methodology include well-history files, daily-rate and flowing-pressure measurements, and laboratory pressure/volume/temperature (PVT) and fluid-analysis reports. The following diagnostic plots are used to identify potential errors or abnormalities in the production data:

  • Rate/pressure/time plot (semilog)
  • Productivity-index/time plot (semilog)
  • Productivity-index/time plot (log-log)
  • Rate/cumulative-production/time plot (log-log)
  • Pressure/rate correlation plot (Cartesian)
  • Rate/pressure/cumulative-production plot (semilog)

In addition to checking the integrity and correlation of production data, the authors also use the following diagnostic plots to establish the reservoir model and flow regimes:

  • Log-log plot
  • Blasingame plot (log-log)
  • Productivity-index/normalized-cumulative-gas-production plot (log-log)

Note that, for the diagnostic plots, an incorrect estimate of the initial reservoir pressure will yield plots that show skewed trends or clumping or scattering of data points, particularly at early production times.

On the basis of the information gathered from the diagnostic plots and well-history files, nonrepresentative production data points that are likely the result of nonreservoir effects or operational changes such as well-cleanup effects, liquid loading, well recompletions, well workovers, or choke changes are filtered. The diagnostic plots are prepared with the filtered production data to identify the flow regimes experienced by a given well. It is of primary importance to recognize if the well is still in transient flow or has already entered boundary-dominated flow because it allows determination of which of the time/rate relation models is appropriate for the given production data.

The authors have elected to focus on the modified hyperbolic and power-law exponential relations as primary time/rate-analysis relations because these provide reasonable estimates when properly calibrated.  Both relations have some constraining (or terminal) decline mechanism. For the modified hyperbolic, an exponential tail is spliced to the typical Arps hyperbolic relation. For the power-law exponential relation, there is also a terminal exponential decline component [but this term is often not used because the form of the power-law (time) term has an implicit terminal decline as well]. For reference, of the two methods, the power-law exponential is almost always the more-conservative reserves estimator.

The flowing material-balance methods use a combination of the gas material-balance relation and the boundary-dominated flow relation for gas flow—which implicitly require that pseudosteady-state/boundary-dominated flow behavior exists in order for these relations to be valid. There are a few software implementations of these relations in the industry, and care must be exercised when using these methods for unconventional reservoirs. Using the latest performance data, it is possible to apply the flowing material-­balance methods to establish a minimum reservoir volume, but this can be (and often is) quite arbitrary. Strong caution is urged when flowing material-balance methods are applied to unconventional oil or gas reservoirs.

Time/Rate Analysis

In the complete paper, the authors demonstrate the applicability of the proposed production-analysis work flow by applying the methodology to two field examples from a shale gas reservoir. These wells were selected because they are representative of either linear or boundary-dominated flow regimes experienced by the other wells in the field. An expanded form of the proposed work flow is also applied to all 30 wells provided for this work, and the 30-year estimated ultimate recovery (EUR) results for these additional wells are included in Table 2 of the complete paper.

Model-Based (Time/Rate/Pressure) Production Analysis

Upon the completion of the time/rate production analysis, the authors conduct analytical (time/rate/pressure) model-based production analysis for a given well and history match the gas-flow rate, the calculated flowing bottomhole pressure, and the cumulative gas production by adjusting the fracture-conductivity, fracture-face-skin, fracture-half-length, and formation-permeability parameters. This is a standard practice in the industry, often referred to as production analysis or rate-transient analysis (RTA). Note that this methodology is quite robust and, while the solutions are well-documented, there are often challenges with being able to match only part of the data. The authors believe that this is because of the nature of the reservoir/completion interface. In this particular application, most, if not all, of the wells are matched with little obvious uncertainty.

This methodology is demonstrated with the two field example cases previously analyzed; four scenarios are prepared for each well in which, for two cases, differing percentage efficiencies of the perforation clusters generating a successful fracture are assumed and, for another two cases, only gas flow or a total gas flow computed from the gas-, water-, and condensate-flow rates is assumed by use of a molar-equivalent calculation. The authors also apply this approach to the remaining 28 field examples, and the 30-year estimated cumulative gas-production volumes for all cases are included in Table 5 of the complete paper.

Two field examples provided in the complete paper demonstrate the issue of nonuniqueness in the solution when generating an analytical model for a multifracture horizontal well to history match the production data for an ultralow-permeability, shale gas reservoir. To illustrate this point, the authors prepare four scenarios for each well that assume differing percentage efficiencies of the fracture clusters generating a successful fracture and use either the original gas-flow rate or an equivalent, PVT-transformed total-gas-flow rate.

Fig. 1 is a schematic of a multifracture horizontal well in which the perforation clusters during the well completion had only a 50% efficiency in successfully forming propagated fractures (i.e., every other perforation cluster produced a single vertical fracture). Fig. 2 is a schematic for a multifracture horizontal well in which the perforation clusters during the well completion had a 100% efficiency in successfully forming propagated fractures (i.e., every perforation cluster produced a single vertical fracture). In Table 5 of the complete paper, results for the analytical models extrapolated to 30-year cumulative production for all 30 cases considered in this thesis are presented. The difference in the 30-year cumulative production volumes ranges from 4 to 63.5%, with the average and median difference being 29.6 and 26.3%, respectively. The disparity in these results is most likely caused by controlling factors such as permeability, fracture half-length, and the number of fractures.

Fig. 1—50% efficiency of perforation clusters in forming a propagated fractured during well completion.

 

Fig. 2—100% efficiency of perforation clusters in forming a propagated fractured during well completion.

Conclusions

A work flow for production-data analysis is proposed aiming to reduce the uncertainty in reserves estimation for shale gas reservoirs by accounting for the following items:

  • Rate/pressure diagnostic plots are created that can be used to identify spurious data points as well as inconsistent flow-rate/pressure trends so that these data points can be removed before analysis.
  • For time/rate diagnostics, the log-log plot of gas-flow rate, decline parameter, loss-ratio parameter, and β-derivative vs. production time is used as the primary diagnostic plot.  
  • For time/rate/pressure diagnostics, the pseudopressure drop-normalized gas-flow rate vs. material-balance/time diagnostic functions are plotted to identify the various flow regimes experienced by the well during production. These plots are also used qualitatively to identify possible incorrect initial-reservoir-pressure estimates.
  • Regression techniques using appropriately selected time/rate models are selected and applied on the basis of the flow regimes observed with the filtered gas-flow-rate data.
  • A history match of the gas-flow rate, the calculated flowing bottomhole pressure, and the cumulative gas-production data are created with the RTA methodology on the basis of an analytical reservoir model.
  • The projected 30-year cumulative gas production volume is used as a proxy for the EUR on the basis of the selected time/rate (decline-curve-analysis) models, as well as the more-rigorous time/rate/pressure analytical reservoir models tuned with production data.
  • The total-gas-flow rate (computed on essentially a molar basis) is used, along with the 100% completion efficiency, to generate the 30-year estimated cumulative production (EUR proxy), which is then used in a regression model to correlate reservoir and completion variables.

On the basis of this work, the authors reach the following conclusions:

  • It is essential to begin the analysis of production data with a thorough review of the well history to identify specific instances of well stimulation, workovers, recompletions, and significant mechanical and operational changes.
  • Production-data analysis is sensitive to both the quality and frequency of the production and pressure data. Diagnostic plots are an efficient means of assessing the integrity of the production and pressure data because spurious or outlier data are easily seen on these plots.
  • The number of fractures used in the time/rate/pressure analytical models has significant effect on the goodness of fit for the log-log and Blasingame plots, but it should be noted that permeability and the number of fractures have a strong correlation; therefore, a previous estimate of the number of productive fractures is essential for unique matches using analytical (or numerical) reservoir models.
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 179119, “A Well-Performance Study of Eagle Ford Gas Shale Wells Integrating Empirical Time/Rate and Analytical Time/Rate/Pressure Analysis,” by A.S. Davis and T.A. Blasingame, Texas A&M University, prepared for the 2016 SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 9–11 February. The paper has not been peer reviewed.

Well-Performance Study Integrates Empirical Time/Rate and Time/Rate/Pressure Analysis

01 February 2018

Volume: 70 | Issue: 2

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