Depositional facies and reservoir characteristics of the Early Cretaceous Lower Goru Formation, Lower Indus Basin Pakistan: Integration of petrographic and gamma-ray log analysis

Dar, Qamar Uz Zaman; Pu, Renhai; Ghazi, Shahid; Ahmed, S.; Ali, Rana Imran; Mehmood, Mubashir

doi:10.1016/j.petlm.2021.09.003

Cited by 10 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predicted DTS curve using various techniques, such as RF, SVM, and DTR, overlapped with the measured DTS for Kadanwari-03 and empirically derived DTS in other wells to evaluate the trends and curve ranges (Figure 8). In comparison to the DTR and SVR algorithms, the RF algorithm was more accurate in dealing with the heterogeneous reservoir sands of LGF (Dar et al, 2021), as it predicted DTS curves that were more consistent with measured curves across all wells (Figure 8).…”

Section: Resultsmentioning

confidence: 95%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

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).

show abstract

Section: Resultsmentioning

confidence: 95%

“…The Cretaceous Sembar Formation serves as a confirmed source rock, with interbedded LGF and Upper Goru Formation shales serving as seal rock (Abbasi et al, 2016). Numerous large and enormous fields, including Miano, Sawan, and Mari, have been discovered in this roughly north-south reservoir fairway (Dar et al, 2021).…”

Section: Geological Settingsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Shore to neritic facies depositions composited by sandstone, siltstone, inter-bedded shale, and thin limestone developed in the Lower Goru Formation [19,20]. Three stratigraphic intervals, named as A, B, and C, are divided in the Lower Goru Formation (Figure 2) [5].…”

Section: Geological Settingsmentioning

confidence: 99%

“…Abundant approaches of reservoir characterization and modeling were applied to hydrocarbon-bearing formations in Indus basin to describe reservoirs and to reduce Energies 2023, 16, 856 2 of 24 surface uncertainties. In previous research, the approaches for conventional reservoirs include two routine techniques: 1. petrophysical analysis for lithology and fluid discrimination [4][5][6][7] and 2. subsurface modeling driven by geophysical data, including seismic attributes and stochastic inversions [6][7][8][9]. Additional aspects, including geomechanics, geochemistry, and fracture networks, are often considered for tight reservoir characterization and modeling [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…clude two routine techniques: 1. petrophysical analysis for lithology and fluid tion [4][5][6][7] and 2. subsurface modeling driven by geophysical data, including tributes and stochastic inversions [6][7][8][9]. Additional aspects, including geomec ochemistry, and fracture networks, are often considered for tight reservoir ch tion and modeling [10,11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reservoir Characterization and an Integrated Approach of Reservoir Modeling for Miano Gas Field, Middle Indus Basin

Yang

Zhao²,

Meng³

et al. 2023

Energies

View full text Add to dashboard Cite

The hydrocarbon-bearing formation of Miano gas field belongs to the Early Cretaceous and it is bounded by two shale intervals, which are considered as maximum flooding surfaces (MFS). The hydrocarbon-bearing interval includes two reservoir units: a tight gas reservoir and its overlying conventional reservoir. Core samples, borehole logs, and well production performance revealed that the two reservoirs present reversed trends in reservoir quality through the gas field without obvious barriers. The average shale volume of the tight gas reservoir changes from 24.3% to 12.2% and the average permeability changes from 32.65 mD to 0.02 mD from the south to north. However, the average effective porosity of the overlaying conventional reservoir increases from 20% to 26% and the average permeability increases from 10 mD to 300 mD. The reversed trends in the two reservoirs lead to challenges in production forecast and development well proposals in the tight gas reservoir. Therefore, reservoir characterization and a predictive reservoir model are essential for further exploitation of Miano gas field. The geological genesis analysis integrating cores, borehole logs, and three-dimensional (3D) seismic data reveals that the producing interval of the tight gas reservoir is tidal-influenced shore facies deposition with intergranular pore space reduced by mineral cementation during burial diagenesis, while the overlaying conventional reservoir is fluvial-influenced deltaic deposition with abundant, well-connected intergranular macropores, which leads to a better reservoir quality. A reservoir model containing both the tight gas reservoir and the conventional reservoir is constructed considering the reservoir nature understanding, and the accuracy of the model is confirmed by reservoir surveillance activities with the simulation model. The study will be critical to the further reservoir development and hydrocarbon production in Miano gas field.

show abstract

Depositional and diagenetic studies of clastic reservoirs zone in the Cretaceous Lower Goru Formation, Sindh Monocline, South Pakistan

Jan,

Shah,

Rahim

et al. 2024

J Earth Syst Sci

View full text Add to dashboard Cite

Depositional facies and reservoir characteristics of the Early Cretaceous Lower Goru Formation, Lower Indus Basin Pakistan: Integration of petrographic and gamma-ray log analysis

Cited by 10 publications

References 28 publications

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

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

Reservoir Characterization and an Integrated Approach of Reservoir Modeling for Miano Gas Field, Middle Indus Basin

Depositional and diagenetic studies of clastic reservoirs zone in the Cretaceous Lower Goru Formation, Sindh Monocline, South Pakistan

Contact Info

Product

Resources

About