Rock Physics Models and Seismic Inversion in Reservoir Characterization, “MUN” Onshore Niger Delta Field

Munyithya, J. M.; Ehirim, C. N.; Dagogo, Tamunonengiyeofori

doi:10.4236/ijg.2019.1011056

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The interpretation of elastic properties, that is, sonic (DTP), DTS, and density (RHOB), is highly important as they are closely related to seismically quantified reservoir properties (Munyithya et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

et al. 2022

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Shear sonic log (DTS) availability is vital for litho-fluid discrimination within reservoirs, which is critical for field development and production. For certain reasons, most of the wells in the Lower Indus Basin (LIB) lack DTS logs, which are modeled using conventional techniques based on empirical relations and rock physics modeling. However, in their extensive computation, these approaches need assumptions and multiple prerequisites, which can compromise the true reservoir characteristics. Machine learning (ML) has recently emerged as a robust and optimized technique for predicting precise DTS with fewer input data sets. To predict the best DTS log that adheres to the geology, a comparison was made between three supervised machine learning (SML) algorithms: random forest (RF), decision tree regression (DTR), and support vector regression (SVR). Based on qualitative statistical measures, the RF stands out as the best algorithm, with maximum determination of correlation (R2) values of 0.68, 0.86, 0.56, and 0.71 and lower mean absolute percentage error (MAPE) values of 4.5, 2.01, 4.79, and 4.65 between the modeled and measured DTS logs in Kadanwari-01, -03, -10, and -11 wells, respectively. For detailed reservoir characterization, the RF algorithm is further employed to generate elastic attributes such as P-impedance (Zp), S-impedance (Zs), lambda-rho (λρ), mu-rho (μρ), as well as petrophysical attributes such as effective porosity (PHIE) and clay volumetric (Vcl) utilizing seismic and well data. The resultant attributes helped to establish a petro-elastic relationship delineated at the reservoir level. Possible gas zones were determined by zones with high PHIE (8%–10%) and low values of other attributes like Vcl (30%–40%), Zp (10,400–10,800 gm/cc*m/s), and Zs (6,300–6,600 gm/cc*m/s). The potential bodies are also validated by low λρ (27–30 GPa*g/cc) cross ponding to higher μρ (38–44 GPa*g/cc).

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Section: Introductionmentioning

confidence: 99%