Video: Characterizing Thinly-Bedded Low Resistivity Reservoirs in Mature Fields
- Sanjeev Rajput (Petronas Carigali Sdn Bhd) | Irmawaty Bt Abdullah (Petronas Carigali Sdn Bhd) | Amit Roy (Petronas Carigali Sdn Bhd) | Aizuddin B. Khalid (Petronas Carigali Sdn Bhd) | Camellia Onn (Petronas Carigali Sdn Bhd) | Ashraf Khalil (Petronas Carigali Sdn Bhd)
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- Society of Petroleum Engineers
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- 2019. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
- Fluid and Facies Probabilities, Low Resistivity and Low Contrast (LRLC), Reservoir Characterisation, Oil Production, Stochastic Inversion
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Low electrical resistivity and low contrast reservoirs (LRLC) pay zones are composed of thinly-bedded laminated layers containing hydrocarbon accumulations surrounded by non-reservoir layers indicating lack of resistivity contrast. These pay zones are difficult to be distinguished at seismic and log scale due to lower vertical and lateral resolution. Traditionally, deep-resistivity logs in LRLC zones read 0.5 to 5 ohm-m. Low contrast pay zone occurs mainly when the formation waters are fresh or having low salinity resulting in a very little resistivity contrast between oil and water zones. Major challenges imposed in LRLC reservoirs include identification, characterization, and evaluation of the hydrocarbon interval, which is usually masked by the lack of resistivity contrast between the hydrocarbon and water zones. The identification and characterization of the lowdown on resistivity pay is essential for the re-development of mature assets for improved oil recovery. This paper deals with the characterization of low resistivity hydrocarbon-bearing thinly-bedded reservoirs from a brownfield.
To unlock the hidden potential of LRLC pay sands in the offshore Sarawak Malaysia, the effective integration of subsurface disciplines including petrophysics, geology and quantitative derivatives from the seismic analysis is vital. This study covers the geological perspective of low contrast reservoirs from an offshore oil field deposited in lower coastal plain settings located within offshore Sarawak Malaysia. An improved understanding of the geological, petrophysical and geophysical parameters was achieved by adopting a holistic and multidisciplinary approach. This includes the integration of core, logs, rock physics modeled parameters, stratigraphic, depositional and lithofacies information along with stochastic inversion derivatives. Acoustic Impedance shows the facies changes in broader terms between producing and non-producing zone.
The paper quantifies rock physics parameter uncertainties for LRLC pay zones and establishes a framework for LRLC reservoir characterization. Stochastic inversion derived P-Impedance and Vp/Vs ratio are used to predict fluid and facies probabilities (Rajput S., 2014) for LRLC reservoirs, which then further integrated with stratigraphic information. The results offered an effective way of establishing analogs of producing and non-producing LRLC zones. Analysis of fluid and facies probabilities derivatives driven surface attributes is a way seismic can potentially contribute to indicating areas of relatively better or worse LRLC reservoir continuity.
Identified LRLC reservoirs proved to be of commercial-quality and increased oil production to the extent of several hundred thousands of barrels over the years and currently producing. Rock physics modeled parameters including AI and Vp/Vs are sensitive to LRLC pay zones and their effective integration with image logs, lithofacies, and seismic inversion lead to reduce uncertainties in infill drilling programs. Geological understanding of the possibility of LRLC occurrences is required to assess oil and gas bypassed oil. Detailed geological features are clearly resolved in high-definition image logs. Low resistivity pay zones present in the main reservoir intervals can be identified by integrating the information from low gamma ray, low impedance, and low resistivity zones collectively. The results of this study show the value of integrated approaches and improvements in reservoir description from stochastic inversion into reservoir models.