Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
Data from seismic to production is integrated to build models to provide estimations of parameters such as petroleum volumetrics, pressure behavior, and production performance (
Reservoir dynamic simulation is the most applied process that integrates all reservoir data, where an Equation of State (EOS) is coupled with the objective to estimate the fluid thermodynamic state at each computational step. The simulation consists of iterative mathematical computations in which the reservoir-defined conditions at the previous time step is an input to determine the properties at the next and subsequent time steps. The calculated pressure is a fundamental variable in each time step, which means that a representative and high level of confidence Pressure Volume Temperature (PVT) model is required to avoid scale-up of errors resulting from fluid pressure estimation.
A PVT modeling includes three main stages: Fluid sample and data acquisition Laboratory analysis and fluid characterization The EOS model.
Fluid sample and data acquisition
Laboratory analysis and fluid characterization
The EOS model.
The emphasis in this work is on the EOS model, which is the fluid model used for the simulation process. The objective of this work is to analyze the main uncertainties associated with typical EOS modeling and defining the level of confidence of these EOS approaches. In this work, some of the most-used approaches for EOS modeling are reviewed. An assessment of these methods is also provided based on their application to actual petroleum fluids with the objective of defining their statistical level of confidence.
First, the study analyzes the sources of critical uncertainties in a PVT EOS model. Second, a statistical number of PVT laboratory studies of petroleum fluids is used to determine the level of confidence of four approaches that are based on the two well-known Peng-Robinson and Soave-Redlich-Kwong EOS. Third, statistical analysis is performed to determine the level of confidence of the different methods. Fourth, a correlation to determine the optimal number of pseudo-components is defined. These steps include: Characterization of fluid and heavy components Tuning Lumping.
Characterization of fluid and heavy components
As a result of this study, one can conclude: The level of confidence of the four analyzed approaches The significance of the difference between the analyzed methods A correlation to determine the optimal number of pseudo-components.
The level of confidence of the four analyzed approaches
The significance of the difference between the analyzed methods
A correlation to determine the optimal number of pseudo-components.
In this work, a statistical analysis over some of the most-used EOS modeling approaches and on a set of petroleum fluid PVTs was performed to determine the level of confidence of four EOS modeling methods. In addition, a correlation was introduced for
Downhole control devices are being widely implemented in fields globally; and, because of the costs involved in their implementation, a robust reservoir performance forecast is necessary. A prerequisite to a sound reservoir development plan is to have a robust history-matched reservoir simulation model. This study involves use of a downhole inflow control device (ICD) well configuration in the reservoir simulation model to perform history matching of a green-field offshore Abu Dhabi. The results of this approach are compared to the results from traditional approaches. The scope of this study is to examine the differences in both history match approaches.
Reservoir A is one of the major reservoirs of a green-field located offshore Abu Dhabi, and is being developed with a five-spot water injection pattern. The producers and water injectors are horizontal wells, which are drilled across different flow units within the reservoir. Because the reservoir is heterogeneous across all the flow units, the injection pattern results in a non-uniform water front. The conventional approach to history matching the well performance is to implement a positive skin factor across the well completions to mimic the effect of the inflow control devices (ICDs) installed in the well: increasing the pressure drop (ΔP) between the formation and the well tubing. In this study, the actual downhole configuration was prepared using well-completion analysis software, followed by use of a next-generation reservoir simulator to run the full field reservoir model for the history matching period.
As the field is being developed on the principles of digital concept, continuous high-frequency downhole pressure data is available in flowing as well as shut-in conditions. The use of this data, coupled with direct modeling of the ICDs in the simulation model, resulted in a significant improvement in the reliability of the history match, as compared to traditional approaches.
This study compares two history matching approaches for fields with wells completed with downhole control devices. The core purpose of this study is to integrate the principles of the digital oil field with conventional history matching techniques, with the ultimate goal of improving the history match.
Unexpected water accumulation (called perched water) can be present inside hydrocarbon bearing reservoirs. In case of limited or poor geophysical data, the prediction of this accumulation may be difficult.
In this paper, a real case is used to show how the presence of perched water was initially supposed and then verified through production data analysis.
During the development campaign of a deep water reservoir in West Africa, a water injector well found an unexpected shallower water table. To understand the nature of this water, the gas while drilling data of two oil producer drilled in the same area of the water injector were analysed. Based on this analysis the last meters of the open hole section of both oil producers were in water. The integration of gas while drilling data, stratigraphy, sedimentology and structural settings knowledge of the area suggested that this water was locally trapped during oil migration, most likely due to the presence of a structural barrier.
The two oil producer wells, located in the supposed perched water area, were successfully started-up. The behavior of both wells was daily monitored to understand and confirm the nature of perched water phenomenon. From day one, the two wells showed water production. After few weeks, the water cut of one well clearly started to reduce. For the other well, the water cut behavior was constant and only after one year of production the declining trend was appreciated. The observed declining trend of water production was the final confirmation that aquifer in this sector of the field is isolated and with limited extension. The water cut trend was also captured in the 3D dynamic reservoir model. In addition, tracers were implemented in the model to identify different water production sources (injected or perched) and to forecast their evolution during the field life.
The literature on perched water is quite limited and usually this kind of phenomenon is detected and described only on the geological side, but the production behavior of this water is rarely observed. This case study is integrating the geological and geophysical knowledge of the field with production data analysis to understand perched water behavior and can be considered a reference for other similar situation.
Hjeij, Dawood (Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University) | Abushaikha, Ahmad (Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University)
Most commercially available simulators use the trivial two-point flux approximation (TPFA) method for flux computation. However, the TPFA only gives consistent solutions when used for K-orthogonal grids. In general, multi-point flux approximation (MPFA) methods perform better under both heterogeneous and anisotropic conditions. The mimetic finite difference (MFD) method is designed to preserve properties on unstructured polyhedral grids, and its development for simulating full tensor permeabilities is also crucial step. This paper compares the performance, accuracy, and efficiency of these schemes for simulating complex synthetic and realistic hydrocarbon reservoirs.
Improved reservoir knowledge is key to extracting additional value from existing oil and gas assets. However, given the uncertainty in the subsurface, it is always a question if our current development strategy is the most robust choice, or if there are alternatives that can further increase the value of our field. This paper presents a novel solution that enables the asset team to answer these questions in a new way. Furthermore, the solution helps teams quickly identify and screen new opportunities that ultimately increase both subsurface understanding and the value of the field. The solution combines a quasi- Newton gradient based numerical optimization scheme with a stochastic simplex approximate gradient (StoSAG) algorithm. Because the algorithm is non-intrusive with respect to the fluid flow simulator, we can directly apply the solution on any flow optimization problem without the need to access the simulator source code. The solution is implemented using a microservice architecture that allows for efficient scaling and deployment either on cloud-based or internal systems. We demonstrate the proposed solution on a field containing 11 oil producers and 7 water injectors by optimizing the water injection and oil production rates. The machine learning algorithm allows us to quickly explore different drainage strategies, given the current understanding and associated uncertainties of the reservoir.
Specifically, the software solution suggests that 6 of the 18 pre-defined well targets are high risk and/or of little value. Running a second development scenario where we do not drill these six wells reduces the investment cost of this field by 163 MUSD and increases the expected net present value per well of the field by 48 percent. Compared with the reactive control drainage strategy approach, we increase the expected net present value of the field by 9.0 %, while simultaneously lowering the associated risk.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
Diatto, Paolo (Eni S.p.A.) | Cerioli Regondi, Anita (Eni S.p.A.) | Doering, Sascha (Eni S.p.A.) | Italiano, Domenico (Eni S.p.A.) | Maffeis, Ivan (Eni S.p.A.) | Marchesini, Marco (Eni S.p.A.) | Martin, Marco (Eni S.p.A.)
With the aim of improving the understanding of production behaviour in a multi-discovery asset and the evaluation of near-field exploration opportunities, an integrated study has been carried out involving three different disciplines: Fluid Thermodynamics (PVT), Organic Geochemistry and Petroleum Systems Modelling (PSM). The synergistic workflow has been undertaken starting from an accurate quality check of the initial dataset related to fluid samples and lab tests. By merging PVT and geochemical data, it was possible to carry out a robust statistical survey and explore correlations across different parameters and features; in this way, strict connection among many physical parameters and some oil maturity and biodegradation indices were identified. In the following step, after geo-referencing the fluid samples in the framework of the Petroleum Systems Model and tracking the locations of the source rocks, a reliable interpretation of the oil expulsion and migration history became possible over the whole reservoir fluid system. Finally, taking into account the simulated fluid phase envelopes, further insights were drawn in terms of the fluid phase behavior in different areas, contributing to reduce uncertainty and exploration risk for future activity in nearby prospects.
Gas injection is a proven EOR method in the oil industry with many well-documented successful field applications spanning a period of more than five decades. The injected gas composition varies between projects, but is typically hydrocarbon gas, sometimes enriched with intermediate components to ensure miscibility, or carbon dioxide in regions such as the Permian Basin, where supply is available at an attractive price.
Miscible nitrogen injection into oil reservoirs, on the other hand, is a relatively uncommon EOR technique because nitrogen often requires a prohibitively high pressure to reach miscibility. Unlike other injection gases, the minimum miscibility pressure for nitrogen decreases with increasing temperature. In fact, in deep, hot reservoirs containing volatile oil, nitrogen may develop miscibility at a pressure similar to the MMP for hydrocarbon gas or carbon dioxide. The phase behavior is more complicated than what can be captured by correlations and hence requires equation-of-state calculations.
Results from a recent EOR screening study in ADNOC indicate that a couple of high-temperature oil reservoirs in Abu Dhabi may be potential targets for miscible nitrogen injection. This paper discusses key aspects of the EOS modeling. Advanced gas injection PVT data are available to enable a fair comparison between nitrogen, carbon dioxide and lean hydrocarbon gas. In this work, we have modelled and analyzed the phase behavior of two volatile oil systems with respect to nitrogen, hydrocarbon gas, and carbon dioxide injection, as part of a reservoir simulation study, which will be covered in a subsequent publication; see
This paper will focus on the application of lithium-ion energy storage solutions (ESS) for offshore oil and gas (O&G) installations. It will discuss the benefits that can be achieved by integrating energy storage in hybrid power plants, using the West Mira semisubmersible installation in the North Sea as a representative case study. West Mira will be the world's first modern drilling rig to operate a low-emission hybrid (dieselelectric) power plant using lithium-ion batteries. The integration of energy storage with the power supply and distribution system of a drilling rig represents an important step towards improving the environmental sustainability of the offshore oil and gas industry by reducing emissions and paving the way to harnessing clean but intermittent renewables, such as offshore wind. Offshore rigs have highly variable power consumption for drilling and dynamic positioning. By incorporating energy storage, it is possible to reduce the runtime of combustion engines and also keep them operating on an optimized combustion level. The installation of an ESS on West Mira will result in an estimated 42% reduction in the runtime of on-platform diesel engines, reducing CO2 emissions by 15 percent and NOx emissions by 12 percent, which is equivalent to annual emissions from approximately 10,000 automobiles. The batteries on West Mira will be charged from the rig's diesel-electric generators and used for supplying power during peak load times. In addition, they will serve as backup to prevent blackout situations and provide power to the thrusters in the unlikely event of loss of all running machinery.