Injectivity decline during sea waterflooding or produced water re-injection is widely observed in North Sea, Gulf of Mexico and Campos Basin fields. The formation damage occurs mainly due to the deposition of suspended solids around injectors and the build-up the external filter cakes in the well bores. The ability to predict injectivity decline accurately is of great importance for project designs and water management. A comprehensive model that incorporates a variety of factors influencing the process is desirable for the prediction. In this paper, a new comprehensive approach for predicting injectivity decline during water flooding is proposed. The deep bed filtration is described by novel stochastic random walk equations. The injectivity decline model takes into account the reservoir heterogeneity and the distribution of solid particles by sizes. It also accounts for the later formation of the external filter cake and its erosion. A piece of software SNY is developed with the proposed model. The model is able to capture the behaviors of the injectors in the field: the initial slow injectivity decline due to the deep bed filtration of suspended particles, the later faster decline due to the build-up of the external cake, and the temporary steady state due to the cake erosion. Stronger normal dispersion or median heterogeneity close to the injector leads to farther penetration of the particles and slower impedance increase. Neglecting the particle population heterogeneity may lead to the underestimation of formation damage and predicts late transition to external cake formation. The impedance at the steady state and the starting time are highly influenced by the cake properties. The impedance and the external cake thickness at the steady state are likely to be higher in horizontal wells than those in vertical wells.
Intelligent Wells are distinguished from conventional wells by being equipped with downhole sensors to monitor the Inflow Control Valves (ICVs) to control the (multiple) zonal flow rates. The data from the downhole sensors monitors the properties of the fluid flowing into the well from the reservoir at a zonal or a well level. The sensor data is analysed to provide the necessary information for the ICVs to be operated in the optimum manner i.e. to increase the hydrocarbon recovery and prevent unwanted fluid production.
This objective is simply stated, but the optimisation calculations required to identify the optimum ICV settings necessitates the repetitive solution of a complex, non-linear problem. Several commercial software providers have made such optimisation algorithms available to the industry to perform this task. However, experience has shown that challenges still arise when they are applied to large, complex models even though these algorithms work well on many simple cases. This is especially true when the optimisation algorithm is combined with a large, multi-well simulation model of multiple reservoirs with a complex, surface production network that is typical of those used today by operators to study real-field cases prior to field development.
Inclusion of the optimisation algorithm not only dramatically increases the calculation time (up to 50 times when compared with the equivalent run without such optimisation); but also stability and convergence problems give additional increases in the running time. More importantly, the combined software will sometimes simply stop, due to erroneous control parameters being provided by the optimisation algorithm. The optimisation algorithm may also return unrealistic results at random time intervals, a problem that can lead to unnecessary complications as it may not be immediately recognised. Such problems are particularly acute if the software is performing multiple realisations, for example when it is being applied to analyse the impact of a multiple field development scenarios or when studying how uncertainty in the reservoir's dynamic and static properties affect the field's production performance.
This paper will present a novel method based on the direct search algorithm for implementing an ICV control strategy. This method was chosen since it is not affected by the convergence problems which have caused many of the difficulties associated with previous efforts to solve our non-linear optimisation problem. Our control strategy will use the current, zonal inflow rate and water cut data to identify the optimal ICV choke positions. The availability of this data reduces the number of possible choke positions that have to be evaluated at each time step by the simulator. Run times similar to the base case are potentially possible while, equally importantly, the optimal value identified is similar to the value returned by the other published optimisation methods referred to above.
This paper outlines the assumptions made and, after exploring the method's use in two single well models for reactive control of oil production from intelligent wells completed with discrete ICVs, its application to a large, reservoir simulation model will be illustrated. The latter application could be implemented rapidly, unlike some other optimisation software, because "tuning?? of the model and/or the method was not required; the control algorithm being always convergent, fast and stable.
The proposed approach is particularly valuable for the analysis of the impact of uncertainty of the reservoir's dynamic a static parameters. This arises because the modified direct search method employed here, being convergent and independent of the initial point, ensures that the result from the multiple realisations are directly comparable because "tuning?? of the algorithm's parameters are not required in the middle of the calculation procedure.
Kashagan is a super giant offshore carbonate field which was discovered in 2000 by a consortium of oil companies (currently, affiliates of): ExxonMobil, ENI, Shell, TOTAL, Conoco-Phillips, INPEX and KazMunaiGaz. The field is located in an environmentally sensitive area of the North Caspian Sea. The field is a deep, large structural relief, over pressured, isolated, carbonate build-up with a high-permeability, karstified and fractured rim and relatively low-permeability platform interior. The field contains a sour, undersaturated light oil with a large gas content. High pressure miscible gas injection is planned for oil recovery enhancement, as well as sulfur management.
No-one doubts the importance of flow assurance in offshore projects in particular. Moreover, it is now well known that gas injection operations require the evaluation of asphaltene deposition risk. The consortium has undertaken extensive evaluations to ascertain the likelihood of any flow assurance risks from subsurface to surface. During the asphaltene risk evaluation, many bottomhole samples have been collected and analyzed for asphaltene content, asphaltene onset pressure (AOP), and SARA (saturates, aromatics, resins and asphaltenes). These continuous analysis efforts have revealed some anomalous results such as AOP being detected from some fluid samples while not being detected from others.
The apparently inconsistent AOP results are critical to understand how to guide flow assurance measures. Therefore, all available asphaltene data were re-assessed in all their aspects to attempt to clarify asphaltene risk. This paper presents a multidisciplinary approach where a synergy between reservoir engineering and geoscience (geology and geohistory) has been developed to explain AOP results for this complex fluid. The results should help flow assurance specialists to better define the asphaltene operating envelope, which will be used for reservoir and production operations optimization. In addition, these results should be useful for optimizing data-surveillance, flow assurance, and for defining new sample acquisition plans. These findings may also be helpful to minimize future sampling and fluids analysis while achieving reliable flow assurance. The paper will show examples of the related flow assurance analyses, and the geological information which were incorporated in the study, resulting in a detailed asphaltene matrix risk profile for this reservoir.
Van Essen, Gijs (Shell International E & P) | Jimenez, Eduardo (Shell) | Przybysz-jarnut, Justyna Katarzyna (IBM T J Watson Research Center) | Horesh, Lior (Shell Intl. E&P Co.) | Douma, Sippe G. (Shell) | van den Hoek, Paul (IBM T.J. Watson R&D Center) | Conn, Andrew (IBM) | Mello, Ulisses T.
Time-lapse (4D) seismic attributes can provide valuable information on the fluid flow within subsurface reservoirs. This spatially-rich source of information complements the poor areal information obtainable from production well data. While fusion of information from the two sources holds great promise, in practice, this task is far from trivial. Joint Inversion is complex for many reasons, including different time and spatial scales, the fact that the coupling mechanisms between the various parameters are often not well established, the localized nature of the required model updates, and the necessity to integrate multiple data. These concerns limit the applicability of many data-assimilation techniques. Adjoint-based methods are free of these drawbacks but their implementation generally requires extensive programming effort. In this study we present a workflow that exploits the adjoint functionality that modern simulators offer for production data to consistently assimilate inverted 4D seismic attributes without the need for re-programming of the adjoint code. Here we discuss a novel workflow which we applied to assimilate production data and 4D seismic data from a synthetic reservoir model, which acts as the real - yet unknown - reservoir. Synthetic production data and 4D seismic data were created from this model to study the performance of the adjoint-based method. The seamless structure of the workflow allowed rapid setup of the data assimilation process, while execution of the process was reduced significantly. The resulting reservoir model updates displayed a considerable improvement in matching the saturation distribution in the field. This work was carried out as part of a joint Shell-IBM research project.
Fractured reservoirs are characterised by a large difference in permeability of the fracture and matrix system. Usually, the matrix contains the bulk of the hydrocarbons while the fractures are the flow paths. These characteristics are challenging for projects aiming at increasing hydrocarbon liquid recovery from gas condensate fields by gas injection. While in fractured oil reservoirs capillary forces (imbibition) or gravity forces can be utilised to improve oil recovery, for gas injection into gas condensate reservoirs, these forces are less important.
The recovery mechanisms were investigated using the properties of a rich gas condensate field located in the Middle East. A fine grid sector simulation model was created in which the fractures and matrix were introduced explicitly.
Without taking diffusion into account, the injected gas breaks through at the producer very fast. The concentration in the produced gas is closely linked to the effective permeability of the fracture divided by the effective permeability of the matrix.
However, taking diffusion into account, the increase in injected gas concentration is much slower. The speed of the increase (for the same pore volume injected) depends on matrix porosity, velocity of the front, fracture spacing and permeability contrast.
The molecules of the injected gas are diffusing into the matrix while the components of the reservoir gas are diffusing towards the fracture. The various components have different diffusion coefficients. Dependent on the injection gas, the dew point pressure in the matrix can be reached (despite the reservoir pressure being constant) and condensate drops out. Hence, the condensate recovery depends on the injected gas.
The results of the study show that neglecting diffusion in fractured reservoirs can result in errors in the condensate recovery of more than 50 %. In addition, the shape of the condensate recovery curve will be incorrect if diffusion is not accounted for.
This study introduces a decision making evaluation method for flexibility in chemical EOR. The method aims to capture the effects of dynamic uncertainties both technical and economic and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the Least-Squares MonteCarlo(LSM) method which is best suited for evaluating flexibility in project implementation. The decision analysed was that of finding the best time to initiate surfactant flooding during the life time of a field under uncertainty. The study was conducted on two reservoir models: 3-D homogeneous model and a 2-D heterogeneous model. The technical uncertainties considered were the residual oil saturation to the surfactant flood, surfactant adsorption and reservoir heterogeneity while the main economic uncertain parameters considered were oil price, surfactant cost and water injection and production costs. The results show that the LSM method provides a decision making tool that was able to capture the value of flexibility in surfactant flooding implementation. The LSM method provides great insight into the effect of uncertainty on decision making which can help mitigate adverse circumstances should they arise. The results found that the optimal policy obtained was reliable and that heterogeneity does affect the optimal policy. It was also possible to consider the value of information using this method.
Shojaikaveh, Narjes (TU Delft) | Berentsen, Cas (Delft U. of Technology) | Rudolph-Floter, Susanne Eva Johanne (Delft U. of Technology) | Wolf, Karl Heinz (Delft U. of Technology) | Rossen, William Richard
The injection of carbon dioxide (CO2) into depleted gas reservoirs and aquifers offer options for CO2-storage. Co2 sequestration is largely controlled by the interactions between CO2, reservoir fluid(s) in place and rock. In particular, the wettability of the rock matrix is a key factor for the fluid distribution and fluid displacement.
In this study, the wetting behavior of CO2-Bentheimer sandstone-water systems was investigated by means of visual contact-angle measurements. The experiments were conducted in a modified pendant drop cell (PDC) that allows captive-bubble contact-angle measurements at elevated temperatures and pressures. Contact angle measures were peformed with water that was fully (pre)-saturated with CO2. Multiple experiments were performed at constant temperature of 318K and pressures varying between 0.1-12 MPA which represent typical in-situ reservoir conditions. The experimental data shows that the contact angle and the size of the bubble converge to equilibrium in time. During this convergence period, the contact angle and the bubble size generally show a slight change as function of time. This can be contributed to the mass transfer and reduction in density of the CO2 during re-equilibration of the system. The experimental data shows a larger dependency of the contact angle on bubble size than on pressure. However, for bubbles with similar size, contact angle shows a slight increase as a function of pressure. However, for bubbles with similar size, contact angle shows a slight increase as function of pressure. All data shows that Bentheimer-water-CO2 systems remain water-wet even at high pressure.
The prediction of multiphase pressure drop during the simultaneous flow of oil, gas and water in vertical tubing strings is crucial in the development and optimum exploitation of an oil field.
In this paper a method is proposed to improve pressure drop predictions by Aziz et al. multiphase vertical-flow correlation. This correlation is theoretically justified as compared to the traditional empirical methods. The present method suggests combining several flow pattern maps with the Aziz et al. correlation in an attempt to achieve the improvement sought. Two field data sets comprising 32 production tests gathered from three different sources in the Middle East and North Africa were used to examine the performance of the various combinations.
The results of this work indicate that the performance of Aziz et al. multiphase correlation can be best improved by replacing its original flow-pattern map with the traditional Duns-Ros flow-regime map and for both data sets used. A significant improvement has been observed giving an overall absolute average percent deviation of 2.16% compared with 5.33% for the original correlation. Also, with this combination the relative performance factor has been reduced to 1.33 from 2.90 for the original Aziz et al. correlation. The efficiency of the Aziz et al.-Duns and Ros combination at high oil production rates, when compared with the original Aziz et al. correlation, was further examined by noticing the improvement gained in statistical measures at higher levels of liquid superficial velocity.
This work represents an addition to the technology of multiphase behavior in vertical pipes and its results will help in a more accurate design of tubing strings in high-rate producing oil wells.
Intelligent wells can improve oil recovery, mitigate risks and avoid unnecessary well intervention in petroleum fields. However, there is no consolidated methodology to evaluate the applicability of intelligent wells and to represent them in commercial simulators, which complicates the comparison with conventional wells. Moreover, there are two main modes of operation of intelligent well valves, reactive and proactive; each one can provide different benefits. In general, proactive control seeks maximum oil recovery, but it requires larger computational effort and greater knowledge of the reservoir than the reactive control. This paper presents a comparison between different configurations of intelligent wells with proactive control and mode operation on/off: (1) five-spot configuration with conventional wells (producer and injectors), (2) one intelligent producer and four conventional injectors, (3) one conventional producer and four intelligent injectors and (4) one intelligent producer and four intelligent injectors, in order to compare the different behaviors. The objective of this study is to evaluate the potential of proactive operation for each type of configuration and the benefits of the intelligent injectors and producer acting separately or together, considering the effects on production and costs of intelligent completion. For this, a genetic algorithm was coupled to a commercial simulator to optimize the proactive control and to search the maximum net present value (NPV), determining the optimum operation control for each valve. The case study consists of one heterogeneous reservoir model, light oil and three economic scenarios (pessimistic, probable and optimistic). Results show that the use of intelligent injector and producer wells together, in this case study, can increase of oil production and decrease of water production, although it may not be the most advantageous alternative because of the higher investment. On the other hand, the configuration using only an intelligent producer well (lower investment) is capable of increasing oil recovery sufficiently, therefore making the best investment with intelligent completion, in this case study.
van den Hoek, Paul (Shell) | Mahani, Hassan (Shell Intl. E&P Co.) | Sorop, Tibi (Shell) | Brooks, David (AAR Energy) | Zwaan, Marcel (Shell Intl. E&P Co.) | Sen, Subrata (Shell India Markets Private Ltd) | Shuaili, Khalfan (PDO) | Saadi, Faisal (PDO)
Polymers exhibit non-Newtonian rheological behavior, such as in-situ shear-thinning and shear-thickening effects. This has a significant impact on pressure decline signature as exhibited during Pressure Fall-Off (PFO) tests. Therefore, applying a different PFO interpretation method, compared to conventional approaches for Newtonian fluids is required.
This paper presents a simple and practical methodology to infer the in-situ polymer rheology from PFO tests performed during polymer injection. This is based on a combination of numerical flow simulations and analytical pressure transient calculations, resulting in generic type curves that are used to compute consistency index and flow behavior index, in addition to the usual reservoir parameters (kh, faulting, etc.) and parameters relating to (possible) induced fracturing during injection (fracture length and height). The tools and workflows are illustrated by a number of field examples of polymer PFO, which will also demonstrate how the polymer bank can be located from the data.