Obadare ('Dare') Awoleke - Research

1. Experimental and theoretical investigation into propped fracture conductivity in low and high permeability environments
  • The development of a pseudo-generalized model for fracture conductivity using factorial designs.
  • Develop a theoretical model for the displacement of fracture fluid from the fracture.
  • Scale up of experimentally determined fracture conductivity values to field fracture conductivity values.

Initial work towards the development of the generalized conductivity model is as shown in Figures 1.1. and 1.2. This correlation is for tight gas reservoirs.

Dare 1 Figure1

Fig. 1.1. Log of fracture conductivity surface plot for varying values of polymer loading and proppant concentration in coded variables. For example, for polymer loading, ‘-1’ means 10 lbs/1000gal and ‘1’ implies 30lbs/1000gals (Awoleke, 2013).

Dare 1 Figure2

Fig. 1.2. Comparison of measured and predicted conductivity using empirical model developed by Awoleke, 2013.

2. Heavy oil reservoirs (production, completion and stimulation related challenges)
  • Determination and optimization of completion strategies in heavy oil reservoirs.
  • Inflow performance relationships in heavy oil reservoirs.
  • Development of appropriate pressure drop correlations for vertical lift performance in heavy oil reservoirs. 
  • Identification, prevention and remediation of formation damage in heavy oil reservoirs.
  • Wellbore stability investigation by coupling production and geo-mechanical models/processes.
  • Water and solids management.

 

3. Data Analysis using statistical and virtual intelligence techniques

Identification of trends and relationships between response variables (ex. hydrocarbon production) and parameters (ex. reservoir, production and operational data) using conventional statistical concepts and machine learning techniques. These analysis techniques are particularly useful when the approximate models used to represent physical systems are inadequate (unconventional reservoirs). An example application is the prediction of water and gas production from counties in the Barnett Shale using completion and stimulation data. Figure 3.1. shows the probabilistic distribution for gas production from horizontal wells in the Parker county of the Barnett Shale.

Dare 3 Figure1

Fig. 3.1. P10, P50 and P90 predictions of gas production for horizontal wells drilled in the Parker County of the Barnett Shale (Awoleke and Lane, 2011).

 

4. The incorporation of uncertainty into the models used by petroleum engineers to predict oil and gas well productivity using time series analysis methods

Reservoir engineers have been more successful in the incorporation of uncertainty into their models. At present, it is the norm to include some kind of uncertainty estimate in productivity predictions. However, predicting and optimizing production for a single well is still the domain of the production engineer. As such, the production engineer needs to be able to incorporate uncertainty into the modeling process. An investigation into the feasibility of this approach was attempted. Figure 4.1. is a summary of the initial work, using production data from a gas well.  Time series data (in this case, production rate versus time) can generally be decoupled into two components, that is, (1) the trend part – output from the reservoir simulator or proxy (like the Arps equation and its more modern variants) and, (2) the stochastic part or the residual, which is the difference between the observed and calculated data. Time series analysis techniques can be used to model the residual.

Dare 4 Figure1

Fig. 4.1. Gas Production in Mscf versus time index (months) for a well in a gas reservoir. Plot shows the workflow associated with using time series analysis techniques to model stochastic portion of production data.

 

5. Application of Monte Carlo techniques and stochastic differential equations to modeling uncertainty in petroleum production or reservoir engineering

At the most basic level, use monte-carlo techniques to generate multiple realizations of well productivity scenarios.
Alternatively, situate single well modeling in a stochastic setting.
Stochastic DE’s have been used to solve the Buckley-Leverett displacement model, which is one of the most difficult equations to solve because of its discontinuous nature (Carter, 2010).
The goal is to have a probabilistic distribution of either flow rate or pressure in a single well setting.

  • Carter, S. J. 2010. A Stochastic Buckley Leverett Model. PhD dissertation, University of Adelaide, Australia (June 2010).