Improved Interpretation of Reservoir Architecture and Fluid Contacts Through the Integration of Downhole Fluid Analysis With Geochemical and Mud Gas Analyses (original) (raw)
2006 AAPG …, 2006
Search and Discovery Article #40229 (2007) Posted February 4, 2007 ... *Adapted from extended abstract prepared for presentation at AAPG 2006 International Conference and Exhibition, Perth, Australia, November 5-8, 2006 ... 1Shell International, E&P, Houston, TX ...
Identification of Fluid Contacts by using Formation Pressure Data and Geophysical Well Logs
Proceedings of 19th International Multidisciplinary Scientific GeoConference SGEM 2019 (ISBN 978-619-7408-77-5, ISSN 1314-2704), 2019
The identification of fluid contacts (gas–water contact—GWC, oil–water contact—OWC and gas–oil contact—GOC) is essential for field reserve estimates and field development and, also, for detailed formation evaluation. For the accurate calculation of some petrophysical parameters, such as porosity, the reservoir interval has to be zoned by fluid type, to account for differences in fluid saturations and fluid properties (e.g., hydrogen index, density, sonic transit time) in the various intervals: gas cap, oil column and aquifer zone. The fluid contacts may vary over a reservoir either because of faults, semipermeable barriers, rock quality variations / reservoir heterogeneity, hydrocarbon-filling history or a hydrodynamic activity. Horizontal contacts are typically taken into consideration, although irregular or tilted contacts occur in some reservoirs. The methods used for determining the fluid contacts include fluid sampling, water and hydrocarbons saturation estimation from geophysical well logs, analyses of conventional or sidewall cores, and formation pressure measurements. The pressure profiles obtained with various formation testing tools over reservoir intervals are, frequently, the primary source of data for defining the fluid contacts. When good quality pressure data can be collected, the fluid contacts can be determined by identifying the depths at which the pressure gradients (pressure versus depth trends) change. This study addresses some issues related to the identification of GWC for two gas fields of Early Pliocene age (Dacian stage), belonging to the biogenic hydrocarbon system of western Black Sea basin-Romanian continental shelf. We show that the identification of these contacts based exclusively on pressure gradients analysis is uncertain or may be inaccurate. The pressure gradients approach should be checked against the results of the conventional interpretation of geophysical well logs (e.g. changes in the computed fluid saturations as a function of depth) and, if available, the results of nuclear magnetic resonance (NMR) log investigations, which are able to indicate the intervals with clay-bound water, capillary-bound water and movable fluids.
Improved Reservoir Characterization in Low-Permeability Reservoirs With Geostatistical Models
This paper is a field application of modem geostatistical reservoir characterization to a complex, low-permeability gas reservoir. Multiwell history matching with this approach is shown to be significantly better than those done with historical approaches. Important opportunities for additional drilling were identified; however, these additional infilllocations required significantly lower well costs to be commercial.
In the context of harder-to-find reserves and rise in development costs, it is vital that reservoir hetero-geneities and compartmentalization be accurately predicted ahead of the drill bit. There are many situations where unexpected compartmentalization negatively impacts reservoir development. This paper used an integration of 3D seismic, well logs, and biostratigraphic data analysis to evaluate compartmentalization in a low well density reservoir (Z-2), onshore Niger Delta. The aim was to identify areas of bypassed hydrocarbon accumulations during production due to compartmentalization. Structural modelling of the Z-2 reservoir identified three intra-reservoir faults that could lead to possible compartmentalization of the reservoir. Z-2 reservoir was interpreted as early transgressive systems tract normal regressive sediments based on sequence stratigraphic techniques used in the modelling. Z-2 reservoir is bounded below and above by layers of shale about 180–200 ft thick, which provides a good seal for the reservoir. Sequential Gaussian simulation algorithm was used to distribute the modelled petrophysi-cal properties in the static model. Modelled porosity, per-meability, and NTG ranges are 5–30 %, 1–10,000 mD, and 0.10–0.98, respectively, through all layers. Z-2 reservoir was divided into two flow units separated by approximately 12-ft-thick shale unit, which could act as a barrier to flow between the zones. Fault analysis was done using Shell structural and fault analysis plug-in in Petrel to determine the shale gauge ratio, fault permeability, and fault zone thickness of the relevant intra-reservoir faults. Fault jux-taposition analysis shows sand-on-sand juxtaposition at the fault tips. Further analysis shows that fault thickness is within the gas crossflow range of (0–0.6 ft) and shale gouge ratio for all three faults falls within the ranges of 0–100 % with a significantly higher percentage of the areas below 35 % in fault 3. Fault 1 will not allow gas crossflow, while \20 % of the juxtaposed areas in fault 2 are within the range to permit gas crossflow. Fault 3 which has a low SGR and high permeability relative to the other faults is not interpreted to be sealing. Fault zone permeability for parts of fault 1 is \1 mD while parts of faults 2 and 3 are [1 mD. The Z-2 reservoir stands the risk of being com-partmentalized into two hydrocarbon accumulations ('X' and 'Y') during production. The total GIIP for Z-2 is 1668 Bscf and with the present well positions and configurations; the production of about 20 % of the GIIP is at risk of being bypassed. Future wells should be planned to appraise 'X' and 'Y' accumulations.
Past, present, and future of basin and petroleum system modeling
AAPG Bulletin, 2018
Basin and petroleum system modeling (BPSM) has had increasing impact on industry decisions related to exploration and new venture opportunities over the last decade. Basin and petroleum system modeling technology, usability, and user group size have grown as a result of its capacity to reduce exploration risk. Based on current statistics, improvements in BPSM have significant potential to reduce future well failures, particularly when caused by lack of petroleum charge. To bring BPSM practitioners from academia and industry together, an AAPG Hedberg Research Conference was organized to discuss the latest developments and issues in this field. A survey was conducted during the conference in Santa Barbara, California (April 2016), and this paper summarizes the results. A key takeaway was an overarching consensus throughout the BPSM community that improved understanding of hydrocarbon migration and more flexible workflows are necessary to better assess charge and migration risk in exploration and new ventures. In addition, BPSM is increasingly used to predict pore pressure and porosity at field scales, which opens new opportunities to integrate BPSM workflows with other technologies, such as seismic rock property analysis and reservoir quality modeling. In this paper, we discuss these and other issues that arose from in-depth discussion and an online survey.
2009
Reservoir well-log and well-core data show that geofluids tend to flow along microfracture-related percolation pathways. These pathways arise from scale-independent, long-range spatial correlation processes at scale lengths from mm (grain-scale) to km (reservoir-scale). Percolation pathway spatial fluctuation power S(k) scales inversely with spatial frequency k, S(k) ~ 1/k. As such the pathways are inherently spatially erratic and unpredictable at all scale lengths. Thus no valid statistical means relates well-scale sample data to reservoir-scale flow structures. It follows that standard flow models based on geometrically-regular geological and/or fracture formations derived from wellscale reservoir samples cannot accurately predict largescale flow patterns. Flow predictions must, instead, be based on in situ reservoir flow data at the scale for which the flow is actually taking place. For reservoir-scale modeling, interwell connectivity is a logical basis for defining flow structur...