Sedimentary Cycles Are Key to Improve Reservoir Performance in Carbonates (Triassic Lower Muschelkalk), De Wijk Gas Field, the Netherlands

2001 ◽  
Author(s):  
J.C.P. Pipping ◽  
T. Carlson ◽  
H.W. Frikken ◽  
P.M. Vallinga
Keyword(s):  
The Netherlands ◽  
Gas Field ◽  
Sedimentary Cycles ◽  
Reservoir Performance ◽  
Lower Muschelkalk

scholarly journals Reservoir Performance Prediction using Integrated Production Modelling (MBAL Software)

2019 ◽  
Vol 8 (4) ◽  
pp. 1484-1489
Keyword(s):  
Performance Prediction ◽  
History Matching ◽  
Material Balance ◽  
Development Planning ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Production Operation ◽  
Gas Field ◽  
Reservoir Performance ◽  
Fluid Properties

Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.

scholarly journals Methane emissions in the Netherlands: The Groningen field

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Tara I. Yacovitch ◽  
Bruno Neininger ◽  
Scott C. Herndon ◽  
Hugo Denier van der Gon ◽  
Sander Jonkers ◽  
...  
Keyword(s):  
Natural Gas ◽  
The Netherlands ◽  
Oil And Gas ◽  
Methane Emissions ◽  
Production Volume ◽  
Offshore Platforms ◽  
Source Characterization ◽  
Gas Field ◽  
Study Region ◽  
Individual Site

The Groningen natural gas field in the Netherlands – one of Europe’s major gas fields – deploys a “production cluster” infrastructure with extraction, some processing and storage in a single facility. This region is also the site of intensive agriculture and cattle operations. We present results from a multi-scale measurement campaign of methane emissions, including ground and airborne-based estimates. Results are compared with inventory at both the facility and regional level. Investigation of production cluster emissions in the Groningen gas field shows that production volume alone is not a good indicator of whether, and how much, a site is emitting methane. Sites that are nominally shut down may still be emitting, and vice-versa. As a result, the inventory emission factors applied to these sites (i.e. weighted by production) do a poor job of reproducing individual site emissions. Additional facility-level case studies are presented, including a plume at 150 ± 50 kg CH4 hr–1 with an unidentified off-shore emission source, a natural gas storage facility and landfills. Methane emissions in a study region covering 6000 km2 and including the majority of the Groningen field are dominated by biogenic sources (e.g. agriculture, wetlands, cattle). Total methane emissions (8 ± 2 Mg hr–1) are lower than inventory predictions (14 Mg hr–1) but the proportion of fossil fuel sources is higher than indicated by the inventory. Apportionment of methane emissions between thermogenic and biogenic source types used ethane/methane ratios in aircraft flasks and ground-based source characterization. We find that emissions from the oil and gas sector account for 20% of regional methane, with 95% confidence limits of (0%, 51%). The experimental uncertainties bound the inventory apportionment of 1.9%, though the central estimate of 20% exceeds this result by nearly 10 times. This study’s uncertainties demonstrate the need for additional research focusing on emissions apportionment, inventory refinement and offshore platforms.

scholarly journals An integrated shear-wave velocity model for the Groningen gas field, The Netherlands

2017 ◽  
Vol 15 (9) ◽  
pp. 3555-3580 ◽  
Author(s):  
Pauline P. Kruiver ◽  
Ewoud van Dedem ◽  
Remco Romijn ◽  
Ger de Lange ◽  
Mandy Korff ◽  
...  
Keyword(s):  
The Netherlands ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Velocity Model ◽  
Gas Field ◽  
Groningen Gas Field

scholarly journals Incorporating dwelling mounds into induced seismic risk analysis for the Groningen gas field in the Netherlands

2021 ◽  
Author(s):  
Pauline P. Kruiver ◽  
Manos Pefkos ◽  
Erik Meijles ◽  
Gerard Aalbersberg ◽  
Xander Campman ◽  
...  
Keyword(s):  
The Netherlands ◽  
Seismic Risk ◽  
Site Response ◽  
Gas Production ◽  
Fragility Functions ◽  
Gas Field ◽  
Amplification Factors ◽  
Reservoir Compaction ◽  
Groningen Gas Field ◽  
The Impact

AbstractIn order to inform decision-making regarding measures to mitigate the impact of induced seismicity in the Groningen gas field in the Netherlands, a comprehensive seismic risk model has been developed. Starting with gas production scenarios and the consequent reservoir compaction, the model generates synthetic earthquake catalogues which are deployed in Monte Carlo analyses, predicting ground motions at a buried reference rock horizon that are combined with nonlinear amplification factors to estimate response spectral accelerations at the surface. These motions are combined with fragility functions defined for the exposed buildings throughout the region to estimate damage levels, which in turn are transformed to risk in terms of injury through consequence functions. Several older and potentially vulnerable buildings are located on dwelling mounds that were constructed from soils and organic material as a flood defence. These anthropogenic structures are not included in the soil profile models used to develop the amplification factors and hence their influence has not been included in the risk analyses to date. To address this gap in the model, concerted studies have been identified to characterize the dwelling mounds. These include new shear-wave velocity measurements that have enabled dynamic site response analyses to determine the modification of ground shaking due to the presence of the mound. A scheme has then been developed to incorporate the dwelling mounds into the risk calculations, which included an assessment of whether the soil-structure interaction effects for buildings founded on the mounds required modification of the seismic fragility functions.

scholarly journals Compaction and subsidence of the Groningen gas field in the Netherlands

2015 ◽  
Vol 372 ◽  
pp. 367-373 ◽  
Author(s):  
K. van Thienen-Visser ◽  
J. P. Pruiksma ◽  
J. N. Breunese
Keyword(s):  
The Netherlands ◽  
Time Decay ◽  
Good Match ◽  
Gas Field ◽  
Decay Model ◽  
Groningen Gas Field ◽  
Rate Type

Abstract. The Groningen gas field in the Netherlands is Europe's largest gas field. It has been produced since 1963 and production is expected to continue until 2080. The pressure decline in the field causes compaction in the reservoir which is observed as subsidence at the surface. Measured subsidence is characterized by a delay at the start of production. As linear compaction models cannot explain this behavior, alternative compaction models (e.g. Rate Type Compaction Model and Time Decay model) have been investigated that may explain the measured subsidence. Although the compaction models considered in this study give a good match to this delay, their forecasts are significantly different. Future measurements of subsidence in this area will indicate which type of compaction model is preferred. This will lead to better forecasts of subsidence in future. The pattern of over- and underestimation of the subsidence is similar for the compaction models investigated and tested. The pattern can be explained by differences in modeled porosity and aquifer activity illustrating the improvement of subsurface knowledge on the reservoir using subsidence measurements.

Sign in / Sign up

Export Citation Format

Share Document