Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

Abstract

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

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

“…Many researchers have successfully used rock physics techniques in the past to predict the DTS log in various fields such as the Middle Indus Basin (Azeem et al, 2015), Barnett Shale Formation (Guo and Li, 2015), North Poland (Wawrzyniak-Guz, 2019), LIB (Durrani et al, 2020), Zamzama Gas Field (Khan et al, 2021), and Mehar Block (Shakir et al, 2021) and further utilized these techniques in improving reservoir characterization based on seismic inversion techniques. Rock physics modeling has provided a decent estimation of the DTS curve, which was evaluated statistically through a QC plot, that is, prediction quality, which assesses the quality of the match between predicted and measured logs ranging from 0 to 1, was equal to 0.78 (Figure 3B).…”

“…The maps generated by taking the average values within reservoir E-sands exhibited low values of Zp (10,400-10,800 gm/cc*m/s), Zs (6,300-6,600 gm/cc*m/s), and Vcl (30%-40%) with high PHIE (8-10%) around the producing well, Kadanwari-01 (Figure 9). Such a response from elastic and petrophysical properties indicates a potential area with good sand quality (Khan et al, 2021), that is, around the producing Kadanwari-01 well and channelized potential sands bound by the polygon (Figure 9). Low values for λρ (27-30 GPa*g/cc) cross ponding to higher μρ (38-44 GPa*g/cc) also support the presence of gas sands around the Kadanwari-01 well and within the polygon (Figure 9).…”

“…Previously, rock physics modeling was used to compensate for these deficiencies by generating a set of Frontiers in Earth Science frontiersin.org consistent elastic logs. In conventional seismic inversion procedures, these modeled logs are utilized to properly delineate the litho-facies of the reservoir, which are further employed for petrophysical property (PHIE and Vcl) predictions (Khan et al, 2021). Depending upon the situation and availability of information about reservoirs, three types of rock physics models are assumed: theoretical, empirical, and heuristic (Avseth et al, 2010).…”

Prediction of Petrophysical Properties Through Comparative Post-Stack Inversion Techniques Using Advance Neural Networking

“…Furthermore, inversion results are trained by the probabilistic neural network algorithm for petrophysical properties estimation i.e., Volume of clay and effective porosities. These petrophysical attributes characterizes the reservoir quality and minimize the exploration risk of targeted potential Lower Goru sands (Khan et al, 2021). Stratigraphic slices of Volume of clay by taking average values within C-interval sand of the Lower Goru Formation are generated for all PNN derived results (Band limited, Model based and Stochastic) (Figure 12a, b and c).…”

“…The workflow for performing this inversion, as shown in (Figure 3), includes extraction of wavelet, seismic tie, generating low-frequency models, petrophysical and PNN training. The probabilistic Neural Networking (PNN) technique helps manage shale packages within the sand (Khan et al, 2021).…”