Al-Hammadi, Mariam Khalil (ADNOC OFFSHORE) | Sinha, Amit Kumar (ADNOC OFFSHORE) | Zakaria, Hasan Mohammed (ADNOC OFFSHORE) | Agrawal, Pawan (ADNOC OFFSHORE) | Al-Badi, Bader Saif (ADNOC OFFSHORE) | Al-Hassani, Sultan Dahi (ADNOC OFFSHORE) | Ahmed, Shafiq (ADNOC OFFSHORE) | Mohammed Khan, Owais (ADNOC OFFSHORE)
Field presented here is giant heterogeneous carbonate field consist of multi-stacked reservoirs, located in offshore Abu Dhabi. This paper presents development plan for one of reservoir. It consists of a very large rich gas cap with oil rim. Current development plan is oil production from oil rim with peripheral water injection with no gas injection and production. Significant amount of data (Core, Seismic, Logs, DST, production test & PTA) during the early production period have been collected. All the data have been integrated to prepare robust Co-development of oil-rim and gas cap to maximize oil and condensate value from the reservoir.
Reservoir consists of three main porous units: Upper, Middle and Lower Units inter-bedded with dense intervals. Generally the best porosity development is seen in the norther part of the field. Sedimentology study was mainly focused in the upper section of the reservoir. Five facies associations were made based on a low inclination ramp depositional model and diagenetic impact honoring the paragenetic sequence. Facies controlled diagenesis was noticed where abundance of echinoderms controlled the different degree of cementation degrading the reservoir quality. Identifying these dominant echinoderm facies zones allows us better well placement by avoiding them. The output of this study was four facies maps with five facies association distribution representing the four cycles seen in the upper part of the reservoir which is the best reservoir section. These facies maps were incorporated for property distribution in the static model.
The reservoir model is constructed using latest acquired seismic and more than 100s of well control for the top of the Reservoir, used as reference structure. Proportional layering was used for different reservoir layers which have been mapped using Isochore. The facies association was distributed using trend map and Vertical proportion curve observed on the Well. Reservoir properties like Porosity and Permeability were distributed using constrained of facies distribution. A newly drilled well also used as for blind test showing the predictability of the model. Dynamic data such PTA and time-lapse MDT has been integrated in the updated model to guide areal and vertical connectivity. Good history matching has been obtained with minimal changes in the static model.
With the updated model different sensitivities of gas cap and oil rim development has carried out in terms of co-development timing, lean gas recycling volume, and inner ring water injection in order to maximize oil and condensate value from the reservoir and avoid oil migration in the gas cap.
Field presented here is located in offshore Abu Dhabi, consisting of multi-stacked reservoirs with different fluid and reservoir properties. In this paper, field development plan of one of reservoir has been presented which was initially planned to be developed with pattern water injection by more than 50 horizontal wells penetrating all the ten oil bearing layers from 9 well head towers. Reservoir consists of under-saturated oil with low gas-oil ratio and low bubble point. Initial 2 years of production was considered as Early Production Scheme (EPS period), during which significant amount of early production data consisting of downhole pressure measurement, time-lapse MDT, vertical interference data, PLT have been collected. Based on EPS data simulation model has been updated. Simulation fits well with the observed pressure gauge and time-lapse MDT data. Updated model gives good prediction for a year of blind test data (including saturation, MDT and porosity) collected from different wells several kilometers away from current development area reflecting a high level of confidence in areal and vertical connectivity representation. Considering other reservoir uncertainties different Development plans have been screened using updated model in order to improve recovery factor and economics. Based on development plan screening study, optimized development option has been chosen for Full Field Development.
Developing tight reservoirs is a challenge, it becomes more challenging when the reservoir is not well known and uncertainties remain in its description. This paper presents how this challenge has been addressed for an offshore tight carbonate reservoir.
A workflow is followed to obtain an optimized development plan for a tight reservoir and to evaluate the effect of uncertainties on field production. Evaluation of uncertainties uses response surface technique; it shows the most influential parameters and supports the data gathering plan to reduce uncertainties.
The reservoir is generally thin with thickness variations across the field and with general thickening toward the north. The porous section of the formation is present in the northern eastern part.
This reservoir is characterized by variable porosity values and low average permeability. Well tests show a saturated oil reservoir with a gas cap.
The field development challenges are to identify a sweet area attractive for field development through representative static and dynamic models. The static model is built using log data from wells penetrations in the porous body. Very few core data are available to build correlation between porosity and permeability. The use of these data was maximized in order to populate the simulation grid with properties. Available dynamic data are used to describe pressure and fluid saturations in the porous zone where most likely fluid contacts values were evaluated and their uncertainty ranges were defined.
As a conclusion of this study, P10, P50, P90 oil production profiles were obtained and the impact of uncertainties was evaluated.
Finally with response surface technique a ranking of the uncertainties based on their effect is obtained. This ranking supports a data gathering plan and helps prioritizing actions needed to reduce the uncertainty in the estimated oil production profile.
Through this work, an example of an optimized development plan for a tight reservoir is presented and response surface technique is used to evaluate uncertainties effect and support the data gathering plan.
Taware, Satyajit Vijay (Reliance Industries Limited) | Taware, Anuradha Gajanan (Reliance Industries Limited) | Sinha, Amit Kumar (Reliance Industries Limited) | Jamkhindikar, Atul (Reliance Industries Limited) | Talukdar, Ramen (Reliance Industries Limited) | Datta-Gupta, Akhil (Texas A&M University)
Reservoir characterization requires accurate estimates of permeability. The commonly used porosity-permeability transforms are often inadequate as permeability is also a function of clay distribution, sorting, pore connectivity, tortuosity and variations in other petrophysical properties. More robust permeability estimation can be made by integrating multiple logs in the transform instead of just porosity.
This paper deals with a novel technique for deriving permeability by correlating multiple well logs with core permeability using non-parametric regression methods. First, we classify the well log data into electrofacies based on the ‘similarity' of their response. This electrofacies classification does not require any artificial subdivision of the data population but follows naturally based on the unique data values reflecting minerals and lithofacies. A combination of principal component and model-based cluster analysis are used to characterize the electrofacies. Secondly, we apply a non-parametric regression technique to predict permeability using well logs within each electrofacies. The main advantage of this technique is that it is primarily data-driven as opposed to model driven and does not require a priori specification of functional forms, which makes conventional multiple regressions difficult and often biased.
The proposed technique was used in a deepwater reservoir system consisting of thick massive sand beds along with thinly bedded sand-shale sequences. The work flow consists of electrofacies identification, facies-wise permeability transform generation, calibration with DST data, and finally permeability population in the geological model using cloud transforms and Sequential Gaussian Simulation (SGS). The permeability transforms generated from core and well log data were validated via blind tests whereby we predicted permeability in another cored well that was not included in the correlation. For further validation, we also used the transform-derived
permeabilities in analyzing the DST results in a blind well where a laminated sand section was tested. A reasonable match of the permeability-thickness product was observed. Finally, the permeability transforms were integrated into a 3-D geologic modeling using SGS and cloud transform. It was observed that our approach correctly captured the porosity-permeability scatter in the geologic model for various facies groups.