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Data Driven Reservoir Modeling

Shahab D. Mohaghegh - July 2017


To efficiently develop and operate a petroleum reservoir, it is important to have a model. Currently, numerical reservoir simulation is the accepted and widely used technology for this purpose. Data-Driven Reservoir Modeling (Also known as Top-Down Modeling or TDM) is an alternative (or a complement) to numerical simulation. TDM uses the “Big Data” solution (Machine Learning and Data Mining) in order to develop (train, calibrate, and validate) full field reservoir models based on measurements (facts) rather than mathematical formulation of our current understanding of the physics of fluid flow through porous media.

Unlike other empirical technologies that forecast production (Decline Curve Analysis - DCA), or only use production/injection data for its analysis (Capacitance Resistance Model - CRM), TDM integrates all available field measurements such as well locations and trajectories, completions and stimulations, well logs, core data, well tests, seismic, as well as production/injection history (including wellhead pressure and choke setting) into a cohesive, comprehensive, full filed reservoir model using artificial intelligence technologies. TDM is defined as a full field model where production (including GOR and WC) is conditioned to all measured reservoir characteristics and operational constraints. TDM matches the historical production (and is validated through blind history matching) and is capable of forecasting field’s future behavior.

The novelty of Data-Driven Reservoir Modeling stems from the fact that it is a complete departure from traditional approaches to reservoir modeling. The Fact-Based, Data-Driven Reservoir Modeling manifests a paradigm shift in how reservoir engineers and geo-scientists model fluid flow through porous media. In this new paradigm current understanding of physics and geology in a given reservoir is substituted with facts (data/field measurements), as the foundation of the model. This characteristics of TDM makes it a viable modeling technology for unconventional (shale) assets where the physics of the hydrocarbon production (in the presence of massive hydraulic fractures) is not yet, well understood.


Although it does not start from the first principles physics, TDM is a physics-based reservoir model. The incorporation of the physics in TDM, is quite nontraditional. Reservoir characteristics and geological aspects are incorporated in the model for as much as they can be measured. While interpretations are intentionally left out during the model development. However, they are extensively utilized during the analysis of model results. Although fluid flow through porous media is not explicitly (mathematically) formulated during the development of Data-Driven Reservoir Models, successful development of such models, is unlikely without a solid understanding and experience in reservoir engineering and geo-sciences. Physics and geology are the foundation and the framework for the assimilation of the data set that is used to develop the TDM.


Top-Down Model is built by correlating (correlation that is conditioned to causation) flow rate, reservoir pressure and fluid saturation at each well and at each time step to a set of measured static and dynamic variables. The static variables include reservoir characteristics such as well logs (gamma ray, sonic, density, resistivity, etc.), porosity, formation tops and thickness, etc. at the following locations:
  • At and around the well,
  • The average from the drainage area,
  • The average from the drainage area of the offset producers,
  • The average from the drainage area of the offset injectors.

The dynamic variables include operational constrains and production/injection characteristics at appropriate time steps, such as:
  1. Well-head or bottom-hole pressure, or choke size, at time step t,
  2. Completion modification (operation of ICV, squeeze off, etc.), at time step t,
  3. Number of days of production, at time step t,
  4. GOR, Water cut and oil production, at time step t-1,
  5. GOR, Water cut and oil production, of the offset wells at time step t-1,
  6. Water and/or gas injections, at time step t,
  7. Well stimulation details.

The data (enumerated above) that is incorporated into TDM shows its distinction from other empirically formulated models. Once the development of the TDM is completed, its deployment in forecast mode is computationally efficient. A single run of the TDM is usually measured in seconds or in some cases in minutes. The small computational footprint, makes TDM an ideal tool for reservoir management, uncertainty quantification, and field development planning. Development and deployment costs of TDM is a small fraction of numerical simulation.


Data-Driven Reservoir Modeling can accurately model a mature hydrocarbon field and successfully forecast its future production behavior. Outcome of Top-Down Modeling are forecast of oil production, GOR and WC of existing wells as well as static reservoir pressure and fluid saturation, all o which are used for field development planning and infill drilling. When TDM is used to identify the communication between wells, it generates a map of reservoir conductivity that is defined as a composite variable that includes multiple geologic features and rock characteristics contributing to fluid flow in the reservoir. This is accomplished by de-convolving the impact of operational issues from reservoir characteristics on production.


Data-Driven Reservoir Modeling is applicable to fields with certain amount of production history, as such, TDM is not applicable to green field and fields with a small number of wells and short production history. Another limitation of TDM is that it is not valid once the physics completely is changed. For example once a TDM is developed for a field under primary recovery, the model cannot be applied to enhanced recovery phase, without retraining the model. Intelligent Solutions, Inc. as the original inventor of Data-Driven Reservoir Modeling (Top-Down Modeling) has recently released a software product for TDM development and deployment called “IMagine”.


There are several case studies that have been published on this topic. Several are presented in this book by SPE, others are published as SPE papers and other archival journals. If you need references to these publications, please Email me at (

Link to the Book (SPE - Bookstore)

Link to the Book (AbeBooks)

Link to the Book (Amazon)