This work presents the methodology of using the "rate-integral" (i.e., cumulative production/time) to assist with "time-rate" well performance analysis or "Decline Curve Analysis" (DCA) for multi-fractured horizontal wells (MFHW). This work provides a methodology for cases where poor data quality make unique analyses/interpretations difficult for decline curve analyses (DCA).

Our primary objectives are as follows:

- Introduce the "qDb-Rate Integral" plot to assist with "time-rate" data analysis/interpretation.

- Derive the time-rate-integral form of current and evolving decline curve analysis models

- Derive the auxiliary diagnostic plotting functions (i.e., D(t) and b(t) parameters) in time-rate-integral space.

- Validate the methodology using simulated unconventional "time-rate" data.

- Provide a systemic and comprehensive demonstration of the rate-integral methodology to field data.

In this work, the primary goal of using the "rate-integral" methodology is to improve the traditional time-rate production diagnostics for the following applications:

- The analysis/interpretation of noisy and/or inconsistent "time-rate" data.

- The prediction of the Arps' b-parameter.

- The calculation and use of derivative-based diagnostic plotting functions (i.e., D(t) and b(t)).

The time-rate-integral approach is not intended to replace traditional production diagnostic and analysis methods, but rather be used as a compliment. This methodology allows for the "smoothing" of "time-rate" data without the introduction of an external smoothing mechanism — we just employ cumulative production functions. The smoother "time-rate integral" data should provide more consistent analyses and diagnostic interpretations.

In terms of deliverables, this work provides:

- The derivation of "time-rate-integral" models and auxiliary diagnostic plotting functions for:

-- The modified hyperbolic DCA relation.

-- The power-law exponential DCA relation.

- Guidance on the use of the "rate-integral" methodology as a complement to traditional DCA analysis.

- "Conceptual" validation of the "rate-integral" methodology using synthetic data.

- Numerous "demonstration" field data examples with comment and guidance.

Although counterintuitive in 2021, one of the major issues facing the oil and gas industry today is data quality. The "time-rate" data acquired from a "typical" unconventional well are often quite erratic (typically due to onsite and offset operational activities). Errors and noise in data are magnified when using a numerical differentiation method [e.g., Bourdet (1989)], for the instantaneous calculation of the loss-ratio [1/D(t)] and loss-ratio derivative [b(t)], which are required for modern DCA diagnostic analyses and interpretations. Using the rate-integral approach, coupled with the proposed "qDb-Rate Integral" plot enables more consistent decline curve analyses to be performed in instances of poor "time-rate" data quality. However; this method should also significantly improve the general task of decline curve analyses, and may be a better approach for the "data analytics" methodologies currently being proposed for decline curve analyses for wells in unconventional reservoirs.

This content is only available via PDF.
You can access this article if you purchase or spend a download.