Lateef Akanji

Prediction of Optimum Length to Diameter Ratio for Two-Phase Fluid Flow Development in Vertical Pipes

We investigate, via numerical simulation technique, the effect of length-to-diameter ratio on transient air-water two-phase flow in vertically upward cylindrical pipe geometry for parameterisation of the pilot scale laboratory multiphase flow rig. Variables such as axial velocity along the leading Taylor bubble, Taylor bubble length and Taylor bubble velocity are considered. A hydrodynamic entrance length required to establish a fully developed two phase flow was critically evaluated. Aperiodic behaviour on time and space dictates the complexity of continuous and unstable gas liquid flow. The porous injection configuration produced small bubble sizes compared to a single gas injection configuration even at higher gas injection rates. Average axial velocity of the leading Taylor bubble of 0.411, 0.424 and 0.451 m/s were obtained for L / D ratios of 16.6, 83.3 and 166.7 respectively. The eccentricity of the axial velocity on the leading Taylor bubble stream and on its nose is perceived from L / D ratio of 166.7.xa0 We obtained a power law function for the radial component of the axial velocity profile in the liquid film ahead of the leading Taylor bubble as , with exponent n =16xa0 for L / D =16.7, n =8 for L / D =83.3 and n =6 for L / D =166.7. Despite the decrease in the exponent as L / D ratio increases, a fully parabolic profile of the axial velocity on the liquid phase ahead of the Taylor bubble is not achieved. This, suggests that further studies on higher L / D ratios should be conducted.

A Neuro-Fuzzy Approach to Screening Reservoir Candidates for EOR

The challenge of discovering new reserves coupled with the current dwindling oil price has necessitated the need to generate and sustain long-term production from existing fields through improved or enhanced oil recovery (IOR/EOR) processes. There is however, no established mechanism to match the thousands of candidate reservoirs worldwide to the subtle and critical variations in reservoir properties that control the success of the many different EOR options. We present a neuro-fuzzy approach to screening potential hydrocarbon reservoirs for enhanced oil recovery (EOR) applications. First, reservoir field data from multiple successful thermal, miscible, chemical and biological EOR projects across different petroleum systems worldwide were trained to establish knowledge pattern and represent it using fuzzy rules. This is achieved by combining fuzzy technique with neural network learning capability to deduce knowledge from the EOR data in a form akin to linguistic rules. Then, the extracted knowledge pattern was validated and used to determine the combination of reservoir properties which could best characterise the key heterogeneities that control EOR success. The model output can be used in screening potential reservoir for EOR application.

Pore-scale analyses of heterogeneity and representative elementary volume for unconventional shale rocks using statistical tools

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 6.7xa0μm resolution with voxels of 990xa0×xa0990xa0×xa01000. 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 300xa0μm 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.

Technical Screening of Enhanced Oil Recovery Methods - A Case Study of Block C in Offshore Angolan Oilfields

This paper presents a technical screening of enhanced oil recovery (EOR) methods by using an Artificial Intelligence (AI) model based on neuro-fuzzy (NF) algorithm. The presented NF approach will enable the user to select a suitable EOR method based on available worldwide successful EOR data and field under investigation. The NF approach presented in this study is a five layered feedforward-backpropagation neural networks where the knowledge pattern is extracted by combining both the searching potential of fuzzy-logic and the learning capability of neural network to make a priori decision. The extracted knowledge from the NF system can be expressed in the form of fuzzy rules by computing weights, number of rules and fuzzy set parameters and validated against reservoir properties data trained from worldwide successful EOR projects. The successfully trained and validated model is then tested on the Angolan oilfield data (Block C) where EOR application is yet to be fully established. The test results show that the NF presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block C) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques.