Case Studies

Brownfield Waterflood Design & Optimization


Have you ever eagerly grown your own fruits and vegetables, only to be let down by a lackluster harvest? You buy a planter box, seeds, and soil. Then invest the sweat equity into getting everything planted and taken care of. Yet, despite your efforts in acquiring supplies and dedicating time and energy to planting and care, you ended up with underwhelming results—small tomatoes, weak herbs, and mediocre broccoli.

Chances are, you overlooked a crucial step—thorough research for success and optimization. Did things grow? Sure. But without understanding factors like plant compatibility, sunlight, and water needs, you may not have maximized the results of your hard work.

Just like growing your own fruits and vegetables to ensure maximum yield, reservoir modelling requires the right technology and information to capture the most out of your primary and secondary oil recovery processes. It’s critical that organizations don't overlook the crucial step of thorough research and optimization, especially with assets that have had prior production.


As with garden soil that has been depleted of nutrients, asset production also declines over time as reservoir pressure depletes. Waterflooding is one strategy that can increase recovery. While analysis isn’t mandatory, failure to evaluate and plan ahead may result in underwhelming performance. Having the right technology and information enables you to capture the most out of your primary and secondary oil recovery processes.

In a recent brownfield waterflood design and optimization study, we began with collecting the necessary geologic and petrophysical data to aid in the creation of geomodels and realizations. Using this static model as a basis for simulation, we added the necessary dynamic data to assess various development scenarios and their performance. Being able to easily quantify the outcomes and differences between scenarios is key in determining risk and implications.  Rapid iterations of this workflow allowed us to quickly identify the optimal field implementation as well as identify and mitigate the potential risks of proposed operations.

Regardless of reservoir type or process, subsurface operations are complex. You need to analyze and understand the impact of several variables simultaneously to make de-risked decisions, fast. Previously, this process could be disjointed as teams are operating in different silos or manually manipulating data to fit into disparate systems, ultimately impacting efficiency and outputs.

The goals from the brownfield waterflood design and optimization study were to:

  • Optimize operations
  • Improve recovery and economic value
  • Assess and mitigate risk

Before implementing the waterflooding technique, the oil recovery from the reservoir was insufficient.

Graph: Showing how waterflooding improved the oil recovery by 42%.
Graph: Showing how waterflooding improved the oil recovery by 42%.


Reservoir Study

The study was conducted on a faulted reservoir with 22 years of primary production. Over time, oil production had declined, and the operator was seeing diminishing returns from the reservoir. To address this issue, waterflooding was proposed as a solution. Careful planning and analysis were crucial to determine the best path forward.

Image: 3D view of the reservoir under study in the following picture.
Image: 3D view of the reservoir under study in the following picture

We constructed the geomodel in Petrel using log and core analysis. This static model was transferred to CMG through our CMG Petrel Plug-in, allowing for quick integration between the geomodelling team and the reservoir engineers. The base model was completed in CMG pre-processing tool, Builder, by adding in the necessary dynamic data such as fluid, relative permeability, and well data. Primary production over the next 15 years was used as a baseline to assess the reservoir performance with no further action.

Next, a preliminary design for waterflooding was assessed. Waterflooding showed promising results, but further questions remained: what is the optimal infill well location, how should the limited water that is available be allocated among injectors, at what depth should infills be completed, and at what time is optimal for drilling the new infills?

To answer these questions, we used CMG’s CMOST sensitivity analysis, optimization, and uncertainty analysis tool to speed up the process. Each of these uncertainties was included as input into the optimizer as guided by the reservoir engineer. CMOST was then able to rapidly evaluate many scenarios and assess the incremental recovery factor and the overall net present value (NPV). Through an automated iterative process, the optimal design was determined, which significantly improved the performance of the planned waterflood.

Image: Net Present Value (NPV) was improved by automatically running multiple simulation models via the optimizer.
Image: Net Present Value (NPV) was improved by automatically running multiple simulation models via the optimizer.

The best-performing scenarios were then further evaluated using CMG’s post-processing tool, Results, to compare and assess the development and operational plan. Cases could be screened to avoid potential risks that may require further engineering analysis.


Our conventional solution allows for multiple disciplines, from geologists to reservoir engineers, to work together. This process starts from geomodelling in Petrel, smoothly transitions to CMG’s Builder, to complete the base dynamic model, and then moving to CMG's optimizer (CMOST) for determining the best path forward.

Utilizing IMEX, the world’s fastest black oil simulator, to build and run the various scenarios allowed us to run a wide variety of asset setups using one solution quickly.

Analysis and comparison of multiple scenarios simultaneously through CMG’s Results, provide a wide array of tools for investigating different reservoir mechanisms - including streamline analysis, isosurfaces, 3D and cross-sectional views, flow vectors, and histograms to make sense of property distributions. Analysis could be accelerated through auto-repeating plots which sets up a particular plot or comparison between runs and automatically repeating it for all applicable wells or models.

Through the CMG Petrel-Plugin, there are different engineering paths that can be leveraged to transition through the workflow. In this study, we moved the geologic models and properties seamlessly from the Petrel to Builder using the plugin and then back to Petrel for additional analysis and comparison.


simulation product use

CMost, Imex, Petrel, builder


Value and Highlights

With an easy workflow and seamless multidisciplinary approach between geomodelling and reservoir engineering team, our black oil conventional solution integrated geomodelling and field optimization to provide valuable insights, increased the return on investment, and provided the information necessary to make critical operational decisions as quickly as possible.

Utilizing this solution for our brownfield waterflood design and optimization study, we were able to improve economics and recovery, while saving time and resources

Key Results

  • Increase waterflood NPV by up to 60%
  • 20% increase in recovery vs base waterflood design
  • Optimization workflow 4 times faster using CMOST
  • Run 10x more cases using CMOST than could be prepared and analyzed manually
  • Greater exploration of potential solutions driving greater returns on investment


  • Improved economics and return on investment
  • Increased recovery from primary and secondary processes
  • Save time and resources through seamless integration
  • Seamless multidisciplinary approach
  • Make informed decisions through physics-based modelling
About This Resource

SPE Paper #: NA

Year: 2023

Software: CMOST

Process: Conventional Reservoir Modelling & Solutions

© Copyright 2023. Customer Success Team. Live presentation webinar, Optimizing your primary and secondary oil recovery processes, 26 June, Calgary, Canada.