Injectivity plays a key role in determining the efficiency of water injection programs. The Injection Fall-off pressure transient analysis is performed to derive injectivity. However, in thin laminated sands, a homogenous model gives average value. For such cases, a more robust multi-layer reservoir modeling approach is required. This paper showcases an integrated workflow utilizing the artificial neural network assisted petrophysical facies modeling for layer determination and reservoir model creation, resulting in higher vertical resolution of injectivity values in heterogenous sand bodies.
The workflow integrates basic and advanced petrophysical logs like lithology log, spectroscopy log, nuclear magnetic resonance logs and elemental analysis logs with the borehole image logs. Artificial Neural Network was used to perform rock typing and facies models by generating the hydraulic flow units and derive the reservoir quality (RQ). Multiple layers of reservoir models were defined based on integrating RQ with the completion quality (CQ). These layered reservoir models were used to perform the modeling and regression in the injection fall-off pressure transient analysis. This approach reduced the time spent on modeling and regression by 30% for a 4-layer reservoir model, resulting in a better match between acquired data and model data.
Using the multi-layer reservoir modeling has increased the vertical resolution of injection fall-off analysis at the same time reducing the time spent on manual modeling and regression work. In heterogenous and thin laminated sand bodies, a homogenous reservoir model provides a single value of permeability and permeability anisotropy. For such cases, it is required to have multiple values of permeability and permeability anisotropy for a better model match. These multiple values are also fed to the numerical reservoir model to check the improvement in injection efficiency.
This paper showcases a unique amalgamation of petrophysical and injection fall-off transient analysis methods for improved reservoir model as well as increased vertical resolution of key reservoir characterization parameters like permeability, skin, permeability anisotropy. The workflow is useful for heterogenous sand bodies and enhanced oil recovery modeling and projects.
