Astute Expository of Vacillation Attributes of Geoseismo-Thermal Ruminative of the Turonian Maastrichtian Fika Shale in Parts of the Bornu Basin, North-Eastern Nigeria
Authors: Egu Daniel Ikechukwu , Ilozobhie Anthony John 
Published: August 2020

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.