In the dash to exploit unconventional resources it has been tempting for some to use or ‘bend’ traditional methods to fit the observed response rather than trying to understand the physics in the belief that some answer is better than none.

At KAPPA we have been resisting this trend. Probably at the cost of some business we have not entered into a Faustian pact and done our best to keep our engineering soul intact.

We strongly believe that given the current state of knowledge, production analysis and the forecasting of unconventional plays, as well as a reserves booking process connected to the physical reality, remain a reservoir engineering challenge. It will remain so until we eventually acquire enough understanding and/or empirical knowledge to use simpler proxies. Decline curve analysis (DCA) still has its place, but only if it is constrained by physics based models, at least on representative wells where sufficient metrology was installed.

Phase one of the not-for-profit KAPPA Unconventional Resource Consortium (KURC) attracted almost thirty collaborative partners from the operators, NOC’s and the service sector. We have developed models and tested hypotheses against members’ data and many of the results have been subsequently integrated into the KAPPA UR workflow. We have tried to understand and implement the real physics as far as we could. One answer typically raises two more questions, and for this reason, and despite the industry downturn, phase two of the KURC is going ahead.

We suggest a UR workflow where we attempt to balance the constraints of having to handle hundreds of wells with the need to understand the physics:

The problem is to capture rate and pressure data from a vast number of wells. To then diagnose the flow regimes, select a representative well and apply rigour to the analysis and then use it as a proxy for a like group to arrive at P10, P50, P90.


Production diagnostics in Citrine can be used to QA/QC and gain an understanding of data sets, then identify the possible characteristic behaviors for quick and efficient analysis of the overall field performance. Citrine’s diagnostic module capabilities include:

  • Assessments of time-rate-pressure data quality and consistency
  • Identification of flow regimes and characteristic behavior
  • Comparison of well performance through the use of multi-well plots
  • Detection of well performance indicators (metrics)
  • Identification of well groups and representative wells
  • Providing up-front information for the initialization of rigorous analysis and modeling


Relevant data are sent from Citrine to Topaze with a few clicks in order to perform Rate Transient Analysis. The data is then used for model-based analysis of rate-time-pressure data using a variety of analytical and non-linear numerical solutions to understand the physical system, history match the production and forecast the EUR. This forecast can then be sent back to Citrine.

This includes specific analysis methods and advanced analytical and numerical models to handle complex well / fracture geometries and the integration of discrete fracture networks (DFN).


In a parallel process the models identified using Topaze can be used in Rubis in order to simulate the multi-well development of the field, including the addition of in-fill wells. In turn these simulations and forecasts may be exported and reloaded by Citrine.

And back to ... Citrine

In order to complete the workflow, Citrine is used as a tool for visualizing the model sensitivities performed in Topaze. Rubis simulations may also be transferred manually.

The Topaze models can be extended to other wells in a specific group using scaling factors. This factor can be used provided that the wells are grouped properly and exhibit a characteristic behavior.

Finally, the long term model behavior can be used in the decline curve analysis (DCA) module of Citrine to provide a practical assessment of the long-term rate decline profile and a range of EUR values. A large variety of decline models are available in this module including conventional Arps and more recent models such as Duong and modified exponential.

Built-in statistical functionality is used to generate percentiles and averages. The results module obtains probability distributions for user-defined well parameters and decline curve results. P10, P50, P90 are set as default but may be selected by the user.

With the development of Generation 5 this workflow will be fully integrated under the new KAPPA workstation.



KAPPA UR Add-on pack explained 12 Aug 2020 2.4 MB Download
KAPPA DDA Book - Unconventional resources chapter 31 Jan 2020 9.2 MB Download