A statistical technique for the pore-scale analyses of heterogeneity and representative elemental volume (REV) in unconventional shale rocks is hereby presented. First, core samples were obtained from shale formations. The images were scanned using microcomputed tomography (micro-CT) machine at 6.7 μm resolution with voxels of 990 × 990 × 1000. These were then processed, digitised, thresholded, segmented and features captured using numerical algorithms. This allows the segmentation of each sample into four distinct morphological entities consisting of pores, organic matter, shale grains and minerals. In order to analyse the degree of heterogeneity, Eagle Ford parallel sample was further cropped into 96 subsamples. Descriptive statistical approach was then used to evaluate the existence of heterogeneity within the subsamples. Furthermore, the Eagle Ford parallel and perpendicular samples were analysed for volumetric entities representative of the petrophysical variable, porosity, using corner point cropping technique. The results of porosity REV for Eagle ford parallel and perpendicular indicated sample representation at 300 μm voxel edge. Both pore volume distribution and descriptive statistical analyses suggested that a wide variation of heterogeneity exists at this scale of investigation. Furthermore, this experiment allows for adequate extraction of necessary information and structural parameters for pore-scale modelling and simulation. Additional studies focusing on re-evaluation at higher resolution are recommended.

In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

In this study, we investigate potential application of environment-friendly bio-surfactants (EFBS) in EOR processes. We assess the prospect of combining the EFBS with controlled salinity (CS) water injection in optimising oil recovery using rhamnolipid and protein-enzyme as case study. Rock component analysis, bio-surfactant solubility in brine of varied concentration and composition, crude oil-brine interfacial tension (IFT) and bio-surfactants emulsification activity test were carried out as part of the preliminary investigations. Following these preliminary analyses, a series of comprehensive core flooding displacement experiments were used to investigate the EOR potential of CSBS injection process. Finally, effluent analyses were conducted to study the effect of this combined process on dynamic oil-brine-rock interactions. Results of the IFT tests using 0.0083-3M brine concentrations show IFT reduction from 3.40-2.5 mN/m with increasing salinity for protein-enzyme while increase in IFT from 0.11-0.34mN/m was observed with increasing salinity for rhamnolipid. However, using a fixed brine concentration of 8.3mM with varied bio-surfactant concentration, IFT reduction with increase in concentration was observed for both of them. Also, the two bio-surfactants exhibited stable emulsion active in varied brine salinity investigated. Protein-enzyme is soluble in varied brine formulation while rhamnolipid solubility was found to be dependent on the brine composition and system pH rather than the ionic strength. Furthermore, from the secondary injection of CS and CSBS, the highest recovery factor of 82.76% was achieved with CSBS (protein-enzyme). However, in the tertiary applications, the highest recovery of 83.40% was achieved in the CS injection. Finally, increased pH, Ca²⁺ and Mg²⁺ concentrations was observed with both CS and CSBS flooding. This suggests reaction between excess cations and previously adsorbed carboxylic group of crude-oil led to increased recovery. Whereas, at residual oil saturation, interaction between bio-surfactant molecules and rock surface resulted in increased water-wetness and release of oil.

A new closed-form analytical solution to the radial transport of tracers in porous media under the influence of linear drift is presented. Specifically, the transport of tracers under convection–diffusion-dominated flow is considered. First, the radial transport equation was cast in the form of the Whittaker equation by defining a set of transformation relations. Then, linear drift was incorporated by considering a coordinate-independent scalar velocity field within the porous medium. A special case of low-intensity tracer injection where molecular diffusion controls tracer propagation but convection with linear velocity drift plays a significant role was presented and solved in Laplace space. Furthermore, a weak-form numerical solution of the nonlinear problem was obtained and used to analyse tracer concentration behaviour in a porous medium, where drift effects predominate and influence the flow pattern. Application in enhanced oil recovery (EOR) processes where linear drift may interfere with the flow path was also evaluated within the solution to obtain concentration profiles for different injection models. The results of the analyses indicated that the effect of linear drift on the tracer concentration profile is dependent on system heterogeneity and progressively becomes more pronounced at later times. This new solution demonstrates the necessity to consider the impact of drift on the transport of tracers, as arrival times may be significantly influenced by drift intensity.