In addition to health, safety, and environmental issues, the presence of mercury can potentially have a huge impact on the economics of a gas development project. A mercury removal unit (MRU) must be designed in order to reduce mercury concentrations to below detectable levels to prevent such issues as liquid metal embrittlement (LME) which could lead to catastrophic events. It is therefore imperative to accurately quantify levels of mercury in a gas reservoir to design gas processing and mercury removal equipment accordingly.Without consistent and precise procedures, accurate quantification is challenging with current technologies used in laboratory apparatus, especially when concentrations of mercury are very low. The conventional method of mercury content quantification is by analysis of samples captured either with wireline formation testers or while drillstem testing (DST) a well. However, these measurements are rarely in line with what is observed once a field is put on production. Loss of mercury due to adsorption by a DST string or by the metal surfaces of sample chambers and sampling tools is significant, especially when the sampling point is thousands of feet away from the producing reservoir. In addition to reaction with metals, test results have also shown how mercury can go undetected if reservoir fluid samples are compromised by small amounts of drilling fluids or mud filtrate. There have been a number of catastrophic failures in gas processing plants attributed to liquid metal embrittlement, such as the explosion at the Skikda LNG plant in Algeria in 1973, or the more recent 2004 New Year's Day Moomba gas plant fire which was confirmed to be due to LME in an aluminum vessel.Workflows detailing procedures for capturing, storing, if required, and analyzing representative gas samples for the quantification of low levels of mercury have been developed, tested, and proven. Results of recent experiments conducted simulating downhole sampling conditions reveal the reasons why there have been numerous cases in which false negative results were obtained from laboratory analyses. Advanced focused sampling methods together with accurate downhole fluid analysis with wireline formation testers have been applied in the field to provide representative reservoir fluid samples for quantification of mercury levels.
A reservoir crude oil contains dissolved gas, liquid and solids (asphaltenes). Together with the Yen-Mullins model and the Flory-Huggins-Zuo equation of state (FHZ EOS), Downhole fluid analysis (DFA) has been successfully employed to delineate asphaltene gradients and reservoir connectivity. Because solubility of asphaltenes in crude oil decreases with increasing gas/oil ratio (GOR) and asphaltene instability, tar mats may be formed by a late gas or light hydrocarbon charge into an oil reservoir. This tar mat is frequently far away from the location of the asphaltene instability, thereby implying asphaltene migration in the formation at much faster rates than diffusive velocities. Therefore, a dynamic mechanism is required to take this phenomenon into account. A dynamic mechanism with gravitational instability induced by gas charges into an oil reservoir is proposed for this purpose. A late gas charge into an oil reservoir can yield a gas cap above initially undersaturated oil. Gas diffusion into the oil column then increases solution gas (GOR) which has the effect of decreasing asphaltene solubility in the crude oil. This can cause asphaltenes to diffuse away from the region of increasing solution gas. Diffusion equations that couple methane, maltene and asphaltene diffusion are consistent with this description. This process can lead to a build-up of asphaltenes at a certain position of the diffusive solution gas front. This increase in asphaltenes, which have been expelled from the high GOR regions, leads to higher density oil than the original oil below this perturbed section of the oil column. In turn, this ‘fluid density inversion’ in the oil column gives rise to gravity currents (gravitational instability – diffusion induced convection) that enable the movement of asphaltenes in porous media over large distances. To validate the proposed mechanism, a diffusive model with moving boundary conditions was developed in conjunction with the FHZ EOS and was applied to gas, maltene and asphaltene multicomponent systems. In addition, this diffusive model is compared with the simplified diffusive model developed by Zuo et al. (2016) and the rigorous diffusive model developed by Shu et al. (2016). The three models predict that the gas/oil contact (GOC) moves up with time due to the swelling effect of a gas charge into a crude oil reservoir and to the loss of gas from the gas cap diffusing into the oil. Density inversion can be produced by the three models by the coupled gas and asphaltene diffusion equations in a relatively wide range of conditions. Significant fluid density inversion is generated at the conditions close to the asphaltene phase instability boundary. This is consistent with the frequent observation of some asphaltene deposition at the gas-oil contact and some asphaltene deposition at the oil-water contact.
Over the past few years there has been a surge of interest in coal bed methane (CBM) resources in many parts of the world. Also known as coal seam gas (CSG), CBM has become an important source of energy because of increasing global demand for cleaner fuels. CBM is distinct from conventional hydrocarbon reservoirs as methane is stored within coals by adsorption. With matrix porosity generally lower than 4%, cleats and fractures are the main conduits for production from coals. Given differences in structure compared to conventional reservoirs, drilling into coal seams requires the use of minimum overbalance and nondamaging fluids. In addition, evaluation of CBM reservoirs has many technical challenges. One of the main challenges is to ascertain coal cleat behavior and estimate permeability and ultimately productivity of a target zone. Traditionally this has been done by production or injection tests using conventional testing techniques. In Australia, a wireline-deployed straddle packer configuration was used to address this challenge, with demonstrated benefits for determining permeability and productivity.Unlike traditional methods of conducting a closed chamber test across a large interval, this methodology uses a straddle packer with a downhole pump in a toolstring deployed on wireline. The packer spacing can be adjusted prior to deployment to suit the expected height of the coal bed to be tested. The tool is capable of both injection into and production from a coal bed interval with a much smaller storage volume compared to conventional test strings. Pressure is continuously monitored in real time ensuring that acceptable limits are not exceeded during either the injection or drawdown phases, to avoid excessive force on a coal seam while maintaining single-phase flow. The analysis of both drawdown-buildup and injection-falloff results reveals the strengths and limitations of the two techniques.
fax 01-972-952-9435. AbstractExploration and appraisal campaigns for deepwater environments are a continuous challenge in today's operations. Data acquisition in such environments requires reservoir information of the highest quality before expensive development plans can be put in place. New technology, real time monitoring and integrated reservoir data are essential to understand such reservoirs. Another challenge presented by thinly bedded reservoirs is the presence of vertical heterogeneity and varying layer flow properties.Wireline formation testers have been commonly used to acquire formation pressures pressure and reservoir fluid samples for a number of decades. Many hardware technologies and interpretation methods have been developed to acquire better quality reservoir information. Dual packer wireline formation testers offer an alternative an additional way to selectively straddle a section of a reservoir and provide the capability to conduct controlled local production and interference as well as to enable the capture of reservoir fluids. Formation permeability, anisotropy, skin factor, vertical connectivity and zonal productivity index are additional reservoir information that can be obtained from a mini-Drill Stem Test (mini-DST) and a Vertical Interference Test (VIT).Pressure transient analysis of a mini-DST data however in such reservoirs is challenging due to the associated uncertainties such as layer flow compartments and flowing fluid viscosity. This paper discusses the use of integrated reservoir information obtained from Downhole Fluid Analyzers (DFA), borehole images, and numerical simulation models to minimize these uncertainties. A systematic pressure transient analysis method for mini-DSTs is also introduced. * Currently with Santos Ltd.Reservoir parameters obtained from mini-DSTs in thinly laminated deepwater reservoirs are then compared with other available static and dynamic reservoir information such as petrophysical data, core analysis, well tests, production logs, and single probe wireline formation tests in order to obtain accurate interpretation results of highest consistency.Field examples will be discussed which show that smaller scale pressure transient tests often have an advantage over full scale well tests testing in terms of providing detailed layer flow behavior, vertical connectivity and flow potential in thinly bedded environments. It will also be noted that the radius of investigation of a mini-DST is limited, typically within tens of feet. This paper demonstrates using field examples that reservoir boundaries can be detected when sufficient radius of investigation is achieved. In addition, the understanding of limitations and advantages will allow the proper selection of test types in order to meet specific objectives and maximize the full potential use of acquired data for field development plans in thinly laminated deepwater environments.
fax 01-972-952-9435. AbstractHorizontal wells often present a substantial challenge in reservoir simulation. In a recent field review, we experienced difficulties modeling long horizontal wells due to the combined complexity of the wells and the reservoir. This reservoir is located in an offshore field that produces oil from a relatively thin oil rim. The reservoir also contains a large gas cap that provides the dominant energy for reservoir recovery. The reservoir is composed of interbedded, shallow marineridge sands with some coarsening-up sequences.A typical horizontal well in this field has perforation intervals of about 1,200-2,900 ft, penetrating depositional sequences from the bottom up, and produces from multiple formations. Wells are placed close to the oil-water contact, sufficiently far from the gas-oil contact to reduce the effects of gas coning and channeling.Due to the large heterogeneity of reservoir properties in different layers, complexity of the geological feature, and the dynamics within the horizontal wellbore, it is very challenging to reproduce the actual production profile in the simulation model. For example, an unfavorable combination of a sump (variation in depth) in the well trajectory and fluid holdups may prevent the toe from producing. The dynamics are difficult to capture without direct production logging and the use of special options of the simulator.In this study, appropriate technologies were applied to solve this problem. Production logging was implemented on four wells. The results revealed that some perforation intervals do not contribute to production. It also exposed allocation problems in the historical production data. This information was used to validate the performance of some wells and to calibrate other wells that exhibit different production profiles. Some simulator options such as multi-segment wells and PLT reporting have been applied to facilitate this calibration process. With this calibration, history matching becomes a more guided process, and the resulting model is more consistent with the actual performance, thus, enhancing its predictive power. This paper describes certain special wellbore dynamic behaviors that have not been covered in the literature. It illustrates the challenges of simulating horizontal wells and how production logging is used to resolve the problem and calibrate the model for better predictive power.
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