Investment and operational planning of gas field development under severe uncertainties in gas reserves poses a major challenge in post parts of Eocene Niger Delta. Such uncertainties include the size of gas in the reservoir, the recovery factor, the required number of wells to be drilled, permeability variations and other criteria. Fekete "FAST" Evolution software was used to run a detailed sensitivity analysis of reservoir properties particularly permeability, in order to determine the number of wells to drill, recovery factor, net present value (NPV) and also the sales of gas produced per well, to effectively ensure maximum hydrocarbon recovery and a good return on investment without posing danger to the reservoir. Results however showed that in terms of recovery factor, scope three had a maximum recovery factor of about 73.61%, in terms of sales gas produced per well. It also had the highest sales gas production of about 10811.5M$, which will yield about 32434.4M$ for the three number of wells investigated as a result of a higher permeability. Also in terms of the net present value, scope one had the highest value of 110633.06M$, for the 12 numbers of wells investigated. It is recommended that since scope three had a better performance than the base case due to higher permeability value, a stimulation job may be carried out at the completion of the project to enhance the permeability of the reservoir, but the cost of carrying out the stimulation job should be considered and analyzed to ensure a good return on investment, before embarking on the decision.
Puissant field planning is increasingly becoming a sophisticated quandary with less emphasis on parametric synergy with reservoir spasmodic acuity. This conundrum leads to inaccurate harbinger of the required number of wells to be drilled for future field development programs from existing production and reservoir data particularly at pressures above the bubble point which is a major sobriety as orchestrated in most recent simulators. The aim of this erudition is to compendiously carry out astute predictive heterodox principles of wellbore aggregates from critical recovery factor parameters for savvy field planning. The main objectives are to glean and develop new propinquities for differential pressures (ΔP), rock compressibilities (Co) and oil formation volume factors (Bo) for predicting the number of wells to be drilled and recovery factors (RF) by equating the simulated results and the theoretical model (Ezekwe, 2010). To elucidate, metaphorize and ruminate new models. Reservoir and economic data was carefully simulated using FAST-FEKETE Evolution software for initial 40 future oil wells. Average results were mathematically correlated with recovery factor model to produce new correlations to quickly re-jig field planning efficiency. Results of matched and validated compressibility factors, differential reservoir pressures and oil formation volume factors were correlated with field data from Ezekwe (2011) model. Results of compressibility factor showed increasing similar 3rd order polynomial converging correlation for both models but gave slight divergence with increasing number of wells and RF. Results of differential pressures gave linearly increasing correlation with number of wells and RF while the new model had a cross-over point at 6435.64 psi for 2 wells but slightly increased divergently with number of wells and RF. Results of oil FVF gave a good similar regression (R2) of 0.999 while both models showed decreasing 3rd order polynomial correlation comparison with number of wells but with slight divergent disparity with increased RF. To further validate the potency of this study, detailed comprehensive paired sample test gave standard deviation, standard error of mean and degree of freedom of 0.00356, 0.0012 and 8 for compressibility factors; 324.7, 102.68 and 9 for differential pressure while the oil formation volume factor gave 0.0067, 0.0021 and 9. The predictions obtained by the new model showed appreciable degree of consistency and accuracy with number of wells and RF. This is perhaps largely hinged on the capacity to cogently infuse field data with theoretical and simulated models effectively. This study has clearly shown that no special technique or rigorous computational procedures is required to plan future number of wells to be drilled in a field or perhaps estimate the required RF. Sequel to this, further research is encouraged to inculcate more correlations based on comprehensive field validation studies to improve the efficacy of this model.
The hydrocarbon potential of the Fika shale in parts of the Bornu basin is severely constrained by extreme geothermal gradients with dire consequences on rock elastic properties and subsurface interpretations of both reservoir and source rock evaluations. This paper investigates the potency of tingeing formation temperatures with rock elastic properties by deriving, validating and characterizing geoseismo-thermal variations from six wells. Mathematical inversion principles and assumptions was used to derive new models by tingeing seismic velocity and time as the first case (T1) while bulk and shear moduli was treated as the second case (T2). Sagacious astute analysis of results of computed average geothermal and geoseismo-thermal gradients within the Fika shale showed some degree of convergence particularly in wells Kinasar, Krumta, Masu and Ziye when a detailed robust test of equality of means of both average gradients was investigated suggesting appreciability with seismic properties. Results of a paired sample T-test of T1 and T2 gave mean standard error, standard deviation, covariance, skewness and kurtosis of 0.01537, 0.03764, 0.07, -0.7 and 0.817 for T1 and 0.85217, 2.08739, 0.07, 0.512 and -1.487 for T2. A revalidation of the new model with Emujakporue (2017) (TEMU) and Ola et al. (2017) (TOLA) showed that Emujakporue (2017) gave appreciable degrees of convergence for both geoseismo-thermal gradients due to seismic velocity and time (TGST1) and due to bulk and shear moduli (TGST2) with respect to TEMU but it was observed that TGST2 showed increasing divergence with depth. This result showed similar pattern with the computed data in the study area. Paired sample test correlation validated results of TEMU and TGST1 gave a combined correlation factor, standard deviation and standard error of mean results of 0.992, 3.41474 and 0.94708 while TEMU and TGST2 gave a combined correlation factor, standard deviation and error of mean of 0.989, 11.82514 and 3.27970. Results of covariance gave 738.833 for TEMU with TGST1 and 437.545 for TEMU with TGST2. This means better approximations of TGST1 with TEMU than TGST2. Results of the Pearson's correlation gave 1.0 for TEMU with TGST1 and 0.992 for TEMU with TGST2. This means better correlation of TGST1 than TGST2 with respect to TEMU. Results of paired sample test of correlations, standard deviation and standard error of mean for Ola et al. (2017) gave 0.976, 16.51011 and 4.57908 for TOLA with respect to TGST1 while TOLA with respect to TGST2 gave 0.966, 4.64432 and 1.28810. Covariance results gave 579.301 for TOLA with TGST1 and 302.539 for TOLA with TGST2. Results of Pearson's correlation gave 1.0 for TOLA with TGST1 and 0.988 for TOLA with TGST2. This signifies TGST1 has a better correlation pattern than TGST2. Conclusively, the vacillational attributes of geoseismo-thermal models due to seismic properties performed better than geoseismo-thermal models due to rock elastic properties. Comparison of the computed models showed relatively good match for TGST1 and TGST2. This novel concept perhaps may open up new challenges on the earlier perceived geothermal gradients of the Bornu basin and similar basin in the world. Sequel to this research, the theoretical basis of this model may be investigated further to incorporate other relevant formation properties sensitive to geothermal gradients.
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