No abstract

Reservoir characterization is an important phase in oil and gas field development, which takes place during the appraisal phase of either a green field or a brown field upon which further development options are considered. Water saturation is a very important parameter in the general description of the reservoir as well as equity determination and dynamic modelling. Numerous equations have been developed which have been used to determine water saturation, but calculated water saturation values have been inconsistent with the saturation values determined from core analysis. This is generally due to their inability to account for the varying distribution of shale in the reservoir and the often incorrectness of their underlying assumptions. The major aim of this research is to develop a model which can be used to determine water saturation values using data from well logs; also, to compare the developed model with other existing models used in the oil and gas industry, using data from core analysis and well logs as the input data; and then finally, to discuss the results of the comparison, using the core-derived saturation values as the bench mark. The model is based on a parallel resistivity model, which is based on the assumption that the conductivity of the sandstone term and the shale term exist in parallel in the shaly-sand reservoir. The shale term in the reservoir of the model is based on the assumption that the clay-bound electrons do not move in the same conductivity path as the sandstone electrons. The shale conductivity term is based on the bound water saturation and the bound water resistivity. The modelled equation was compared in two scenarios using well log data and core data from two different reservoirs, and the model showed consistency in predicting the average water saturation in both reservoirs. The results of the comparison were positive for the modelled equation, as it gave coherent results in both comparison scenarios and matched reasonably the average water saturation of the selected reservoirs. This developed model can serve as an accurate means of determining water saturation in reservoirs, especially for reservoirs with similar characteristics as the selected reservoirs in this research.

Drilling fluids are the most important materials in drilling operations, therefore improving the properties of these fluids are very essential in order to meet up with the increase in demands and required standards. In this experimental study, Solanum tuberosum formulated biopolymer was used to improve the water based mud rheological properties and artificial neural network predicted data for (PV) plastic viscosity, (AP) apparent viscosity and (YP) yield point. Artificial neural network (ANN) was used to train the rheological properties of the formulated mud and the network developed predicted the rheological properties of an untrained combination of bentonite and modified biopolymer. The main target is to regenerate or predict the rheological properties of the formulated mud; (AP) apparent viscosity, (YP) yield point and (PV) plastic viscosity generated originally from experimental procedures but this time using the ANN. The mean average error target was set to around 5-10%. As a model for choosing the best ANN architecture for predicting target value, two statistical parameters, mean squared error (MSE) and correlation coefficient (R2) were utilized. A system with the lower estimations of MSE and the higher estimations of R2 gives more precise forecasts. Three different network were created and the two statistical parameters were used to determine the best number of neurons in the hidden layer. The developed neural network with best prediction has Root Mean Square Error (MSE) of 1.25221 and overall correlation coefficient (R2) of 0.99373 for the predicted plastic viscosity, yield point and apparent viscosity

Geoelectrical resistivity imaging technique was conducted at Abule-egba open dumpsite, Lagos with intentions to monitor the movement of the contaminant leachate within the near surface. The two survey traverses (T1 and T2) of 2D electrical resistivity tomography were conducted using Wenner array configuration with minimum electrode spacing of 1 metre and maximum data levels of seven. The inversion models revealed the two delineated geoelectrical layers which are sandy-clay and sand units. The leachate fluids has the resistivity values of 5.75 – 10.4 Ωm at depth range of 2.5 – 4.0 metres along T1 and T2 moving from NW to the SE section. The leachate fluids are capable of polluting shallow groundwater aquifer within the study area and the residents are advised to drill deeper in the subsurface for clean and portable groundwater resources.

Reservoir characterization deals with the description of the reservoir in detail for rock and fluid properties within a zone of interest. The scope of this study is to model lateral continuity of lithofacies and characterize reservoir rock properties using geostatistical approach on multiple data sets obtained from a structural depression in the bight of Bohai basin, China. Analytical methods used include basic log analysis with normalization. Alternating deflections observed on spontaneous potential (SP) log and resistivity log served as the basis for delineating reservoir sand units and later tied to seismic data. Computation of variogram was done on the generated petrophysical logs prior to adopting suitable simulation algorithms for the data types. Sequential indicator simulation (SIS) was used for facies modeling while sequential gaussian simulation (SGS) was adopted for the continuous logs. The geomodel built with faults and stratigraphical attitude gave unique result for the depositional environment studied. Heterogeneity was observed within the zone both in the faulted and unfaulted area. Reservoir rock properties observed follows the interfingering pattern of rock units and is either truncated by structural discontinuities or naturally pinches out. Petrophysical property models successfully accounted for lithofacies distribution. Porosity volume computed against SP volume resulted in Net to gross volume while Impedance volume results gave credibility to the earlier defined locations of lithofacies (sand and shale) characterized by porosity and permeability. Use of multiple variables in modeling lithofacies and characterizing reservoir units for rock properties has been revisited with success using hydrocarbon exploration data. An integrated approach to subsurface lithological units and hydrocarbon potential assessment has been given priority using stochastic means of laterally populating rock column with properties. This method finds application in production assessment and predicting rock properties with scale disparity during hydrocarbon exploration.