The traditional trial and error approach of history matching to obtain an accurate model requires engineers to control each uncertain parameter and can be quite time consuming and inefficient. However, automatic history matching (AHM), assisted by computers, is an efficient process to control a large number of parameters simultaneously by an algorithm that integrates a static model with dynamic data to minimize a misfit for improving reliability. It helps to reduce simulation run time as well.
Particle Swarm Optimization (PSO) is a population based stochastic algorithm that can explore parameter space combined with the least squares single objective function. The process of AHM can adopt parameterization and realization methods to reduce inverse problems. In this study, realizations of various reservoir properties such as porosity, net to gross, relative permeability, horizontal and vertical permeability, and aquifer size were chosen for controlling throughout the AHM. History matching was conducted to validate the efficiency of each method. The guidelines for optimized AHM with a stochastic algorithm are also disccussed.
The realization and parameterization methods improved matching results in a full-field application with resulting in a reduced misfit and in less. A stochastic algorithm generates multiple models to deduce control parameters to reduce a misfit. In this study we identified that PSO converged effectively with updated control parameters. The optimized AHM improved the accuracy of a full-field model although some misfit remained in the match to bottomhole pressure.
We found that updating with too many parameters makes the problem difficult to solve while using too few leads to false convergence. In addition, while the simulation run time is critical, a full-field simulation model with reduced computational overhead is benefitial.
In this study, we observed that the PSO was an efficient algorithm to update control parameters to reduce a misfit. Using the parameterization and realization as an assisted method helped find better results. Overall this study can be used as a guideline to optimize the process of history matching.
Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
Junwen, Wu (Sinopec Research Institute of Petroleum Exploration and Development) | Wenfeng, Jia (Sinopec Research Institute of Petroleum Engineering) | Rusheng, Zhang (Sinopec Research Institute of Petroleum Exploration and Development) | Xueqi, Cen (Sinopec Research Institute of Petroleum Exploration and Development) | Haibo, Wang (Sinopec Research Institute of Petroleum Exploration and Development) | Jun, Niu (Sinopec Research Institute of Petroleum Exploration and Development)
The high efficient foam unloading agent was developed to solve the problem of unloading of liquid loading gas well with high gas temperature, salinity and high concentration of H2S gas and gas condensate. The Gemini anionic surfactant with special comb structure was synthesized as foaming agent molecule, the modified nanoparticles with certain size and degree of hydrophobicity was adopted as solid foam stabilizer, and the fluorocarbon surfactant was designed and synthesised as gas condensate resistance components. The indoor experiment results show that the foam unloading agent showed good foaming and foam stabilizing ability when the temperature is as high as 150°C, salinity is up to 250000 ppm and H2S concentration up to 2000 ppm. Besides, the foam unloading agent present good liquid carrying ability when the volume fraction of gas condensate is as high as 50%. The field test of this foam unloading agent in Longfengshan north 201-XY well shows that, the average gas production increased from 7256 m3/day to 11329 m3/day, increased by 56%, the average differential pressure between tubing and casing dropped from 2.66 MPa to 2.38 MPa, fell by 10.5%, both liquid yield and gas production are obvious, which prove that the foam unloading agent can meet the demand of drainage gas recovery for high content gas condensate gas field.
Agent-based models (ABMs) provide a fast alternative to traditional partial differential equation (PDE)- based oil reservoir models by applying localized inexpensive simulations, rather than solving a partial differential equation at every time-step. However, while there have been theoretical and numerical results obtained with ABMs in social science applications, the accuracy of ABMs has not been analyzed in the context of oil reservoir modeling.
Some of the things that Sandy is designed to answer quickly include details around faults or the average porosity of a reservoir, which Laigle said is a “simple question to ask, but quite complex to answer.” In January, BP invested $5 million in the young company to help its upstream unit achieve a 90% reduction in the time its engineers spend on data collection, interpretation, and simulation. Behind Sandy are a number of programming elements that have been proven for years in the consumer sector. This includes the knowledge-graph technique that is core to Google’s ability to link relevant but unconnected pieces of information together with its search tool. In addition, the startup is creating a suite of intelligent agents to carry out domain-specific tasks with the data.
Vazquez, Oscar (Heriot Watt University ) | Ross, Gill (Chrysaor) | Jordan, Myles Martin (Nalco Champion) | Baskoro, Dionysius Angga Adhi (Heriot-Watt University) | Mackay, Eric (Heriot-Watt University) | Johnston, Clare (Nalco Champion) | Strachan, Alistair (Nalco Champion)
Oilfield-scale deposition is one of the important flow-assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale, such as reducing sulfates in the injected brine, reducing water flow, removing damage by using dissolvers or physically by milling or reperforating, and inhibition, which is particularly recommended if a severe risk of sulfate-scale deposition is present. Inhibition consists of injecting a chemical that prevents the deposition of scale, either by stopping nucleation or by retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line or bullheaded as a batch treatment into the formation, commonly known as a scale-squeeze treatment. In general, scale-squeeze treatments consist of the following stages: preflush to condition the formation or act as a buffer to displace tubing fluids; the main treatment, where the main pill of chemical is injected; overflush to displace the chemical deep into the reservoir; a shut-in stage to allow further chemical retention; and placing the well back in production. The well will be protected as long as the concentration of the chemical in the produced brine is greater than a certain threshold, commonly known as minimum inhibitor concentration (MIC). This value is usually between 1 and 20 ppm. The most important factor in a squeeze-treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical-return concentration is greater than the MIC.
The main purpose of this paper is to describe the automatic optimization of squeeze-treatment designs using an optimization algorithm, in particular particle-swarm optimization (PSO). The algorithm provides a number of optimal designs, which result in squeeze lifetimes close to the target. To determine the most efficient design of the optimal designs identified by the algorithm, the following objectives were considered: operational-deployment costs, chemical cost, total-injected-water volume, and squeeze-treatment lifetime. Operational-deployment costs include the support vessel, pump, and tank hire. There might not be a single design optimizing all objectives, and thus the problem becomes a multiobjective optimization. Therefore, a number of Pareto optimal solutions exist. These designs are not dominated by any other design and cannot be bettered. Calculating the Pareto is essential to identify the most efficient design (i.e., the most cost-effective design).
A major challenge in carbonate reservoirs is the highly-fractured nature of the rock. The flow rate may be high or low depending on the targeted fracture clusters. In addition, it is possible that flow rates vary from one region of the reservoir to another. Smart wells furnished with smart completion strategy presents great prospects to produce such reservoirs intelligently, thereby, helping to deal with heterogeneities rather smartly. It is established that early water break-through occurs when multi-lateral wells are completed with constant choke settings, and therefore one way to mitigate this problem is using smart completions that manage the unexpected production through fractures, thereby increasing ultimate recovery. The early water breakthrough is obvious because if a lateral section intersects a clusters of fracture zone, there is a possibility that these fractures may connect with the water zone that may trigger the breakthrough. This can be managed by preferentially regulating production from manifold laterals.
The evident communication among the various laterals of the mother bore raises difficulty in optimizing the production from the variable productivity intervals. In theory, the optimization scheme of smart completion involves different constraints, nevertheless, the settings of the smart inflow control valve (ICV) is the single most important parameter that may prove to be the differentiating factor between a high producing well to a poorly producing one. This study engrosses its effort on the reservoir engineering characteristics of finding the optimum choke setting that would lead to maximum recovery.
Computational Intelligence through Particle Swarm Optimization (PSO) is utilized as the integral algorithm to determine the optimal ICV configuration for a fishbone well in a naturally fractured carbonate reservoir. A commercial black oil simulator was used to determine the objective function; whose role here is to evaluate the fitness of a configuration of the choke; this was carried out under a workflow programmed in the MATLAB programming language that coupled the optimization algorithm with the numerical simulator. A single fishbone well, having 15 laterals was studied in order to see the effect of the fracture network on the water breakthrough and consequent impact on recovery.
Three different scenarios are developed to see the impact of optimization; a base case employing only multilateral well technology without the smart well completion, a smart well completion scheme with no optimization and finally the optimized smart well completion. The results very sequentially clarify the need for not only optimization but also highlights the role of intelligent completions for wells in the reservoir being studied. It is evident that without using smart wells, the water breakthrough is relatively earlier and produces less hydrocarbons, but as the use of smart wells is incorporated, the results start improving and for complete optimization scheme of the ICVs, it is observed that the recovery has increased by almost 80% from 21% to 38%. Moreover, the time to water breakthrough and eventually the cumulative water cut has also been managed quiet significantly.
Yousuf, Arif (Sprint Oil & Gas Services) | Temuri, Saqib Jah (Sprint Oil & Gas Services) | Raza, Mustansar (Sprint Oil & Gas Services) | Dar, Afnan Ahmed (Sprint Oil & Gas Services) | Siddiqi, Sarmad S. (Pakistan Petroleum Limited) | Hammad, Muhammad (Pakistan Petroleum Limited) | Ahmed, Muneeb (Pakistan Petroleum Limited)
In matrix acidizing of carbonate formations, acids are used to create wormholes that connect the formation to the wellbore. Hydrochloric acid, formic or acetic acid, or mixtures of these acids are commonly used in matrix acidizing treatments of carbonate reservoirs. However, the use of these acids exhibits some major limitations including high and uncontrolled reaction rate, face dissolution and corrosion to the completion goods, especially those made of chrome-based tubulars (Cr-13 and duplex steel), and these problems become severe at elevated temperatures.
This paper presents a case study of a post datafrac operation wherein a state-of-the-art stimulation system, based on a chelating agent, is deployed in a matrix stimulation treatment of a low temperature tight carbonate reservoir for the first-time in the country. The new stimulation fluid allows the operator to optimize datafrac and wellbore stimulation in a single treatment. The approach also aids the project to be cost-effective and financially feasible, particularly in a low-budgetary environment.
Literature review comparing selected chelating agent and conventional acids is also described in this paper to support the approach adopted in abovesaid case study.
In matrix acidizing of carbonate formations, acids are commonly used to increase matrix productivity by creating wormholes (new flowing channels) that connect the formation to the wellbore. Hydrochloric (HCl), formic or acetic acid, or mixtures of these acids are widely used in matrix acidizing treatments of carbonate reservoirs. However, in present time, a new set of chemical stimulation fluid called Chelating Agent (CA) is introduced in industry to treat limestone and sandstone formations even at high temperature. Chelating agent is widely acclaimed over the conventional acids due to its physical and chemical prowess.
The main challenge using HCl in stimulating carbonate formation is face dissolution caused by the rapid reaction of HCl with formation minerals. This challenge aggravates when temperature goes above 250 deg F where wormhole propagation is minimum and wormhole widening is generally observed.
Azevedo, Leonardo (Cerena/Decivil, Instituto Superior Técnico) | Demyanov, Vasily (Institute of Petroleum Engineering, Heriot-Watt University) | Lopes, Diogo (Cerena/Decivil, Instituto Superior Técnico) | Soares, Amílcar (Cerena/Decivil, Instituto Superior Técnico) | Guerreiro, Luis (Partex Oil & Gas)
Geostatistical seismic inversion uses stochastic sequential simulation and co-simulation as the perturbation techniques to generate and perturb elastic models. These inversion methods allow retrieve high-resolution inverse models and assess the spatial uncertainty of the inverted properties. However, they assume a given number of a priori parametrization often considered known and certain, which is exactly reproduce in the final inverted models. This is the case of the top and base of main seismic units to which regional variogram models and histrograms are assigned. Nevertheless, the amount of existing well-log data (i.e., direct measurements) of the property to be inverted if often not enough to model variograms and its histograms are biased towards the more sand-prone facies. This work shows a consistent stochastic framework that allows to quantify uncertainties on these parameters which are associated with large-scale geological features. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions inferred from geological knowledge are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure, we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions of potential values are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone.
Sergeev, Vitaly (VI-ENERGY LLC) | Kim, Ijung (Western New England University) | Zeigman, Juri (Ufa State Petroleum Technological University) | Yakubov, Ravil (Ufa State Petroleum Technological University)
This article represents the results of the research and development project that has been conducted with the goal to create an innovative and environmental water-blocking agent for the enhancement efficiency of the improved oil recovery methods. The innovation of the developed water-blocking agent lies in the combination of unique physical and chemical properties: high thermal stability (140 C), improved rheology (viscoplastic properties), outstanding surface activity (regulation of the wettability of rock surface), and selective blocking effect. The key factor to combine all of these properties in one solution is to apply colloidal silicon dioxide nanoparticles with modified surface as a stabilizer and surface-active phase in the emulsion system. Also, a technology on the new water-blocking agent for the effective application in the intensification of oil production (well stimulation, IOP) methods has been developed, and well-tested in the field. For the investigation of the unique properties of the new emulsion systems with colloidal silicon dioxide nanoparticles, different types of laboratory experiments were carried-out, including coreflooding tests on the oilfield cores by using the facility with parallel coreholders for stand modelling of the developed technology in the IOP. The main task of the IOP technology is to redistribute the filtration inflows in the near-wellbore zone by selectively blocking the most permeable water-saturated intervals and to penerate through the less permeable interlayers of the bottomhole zone by the acid composition.