Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Latin American & Caribbean Petroleum Engineering Conference
A New Mathematics Model and Theory for Heavy-Oil Reservoir Heating by Huff 'n' Puff
Dou, Hong'en (RIPED,PetroChina) | Chang, Yu Wen (Research Inst. Petr. Expl/Dev) | Yu, Jun (Liaohe Oilfield E&D Research Inst.) | Wang, Xiaolin | Chen, Changchun (RIPED,PetroChina) | Ma, Yingwei (China U. of Petroleum)
Abstract This paper presents a new mathematic model to calculate heated reservoir area based on the heat balance principle in order to determine the well spacing for thermal recovery. The calculation result of the new model was compared with the classical models of J.W Marx and R. H. Langenheim (Petroleum Transactions, AIME Vol. 216, pp. 312–315, 1959), B. T Willman (JPT, July 1961, pp. 681–696), and Farouq. Ali (1970), using data from Liaohe heavy crude oilfield, China. The results showed that the new model is more accordable with oilfield actual condition than the three classical models. Also, this research reveals a new theory on huff ‘n’ puff, which is that the heated radius of the heated region is expanded from the first cycle to the fourth cycle of huff ‘n’ puff; in this case, the heated front was expanded with the increase of cycle. However, subsequent cycles (from fifth cycle to tenth cycle of huff ‘n’ puff) repeat the heating of the previous heated areas, and the expanding heated area of the next cycle is smaller than the last cycle, and the new heating region should hardly be expanded after the 10th cycle. The authors point out the development results of heavy oil, extra heavy oil and super heavy oil deteriorate due to this reason. In addition, the authors emphasize that the traditional huff ‘n’ puff for producing heavy oil, especially for extra-heavy oil and super-heavy oil has to be changed using new technique methods after the fourth cycle. Finally, a suitable well spacing for thermal recovery with huff ‘n’ puff was obtained. The new theory was proved by heavy crude/extra heavy oilfield development. Introduction Huff ‘n’ puff has been used in heavy oil reservoir development since the 1960's. Heavy oil production techniques have been advanced greatly in Canada and Venezuela. In the early 1980s, many thermal recovery techniques were developed, such as insulation tubing, high temperature packer and measurement instrument of thermal parameters. In Liaohe oilfield, China; the reservoirs are at depth from 800 to 2000 m. Heavy oil development was a great success, with production rate reached 700×104 tons per year. During the 25-year production period (1980 to 2005), 20 % of the oil in place was produced. The mechanism of production was a combination of solution gas expansion and huff ‘n’ puff, as the cycle of huff ‘n’ puff is more and more, the development result become worse and worse. Currently, huff 'n ‘puff has exceeded 15 cycles in some wells. The adjustment of oil development strategy faces great challenges, especially in planning well spacing for different types of reservoirs in the oilfield to reach the maximum thermal recovery. The heating front of steam injection, swept region of hot water and the determination of the heated radius are the main parameters to be taken account for designing the well spacing of the heavy oil reservoirs during huff ‘n’ puff. Well spacing for thermal recovery will not be determined if the heated radius should not be calculated accurately. Therefore, after the three classical models of J. W. Marx-R. H. Langenheim (1959), B. T Willman (1961) and Farouq. Ali (1970) was analyzed [1–3], and the paper presents three generalization calculation equations and a new model for calculating the heated radius of the thermal recovery. Analysis of Classical Model Many researches have developed the theory for the estimation of the heated radius of the heated region and design of well pattern by some scholars [4–8], and the three classical models of Marx-Langenheim (1959), Willman (1961) and Farouq Ali (1970) were used and introduced widely. However, the three models were not analyzed systematically, and the conclusions and recognizing of the heated radius of actual oilfield were not presented in past published papers. Also, the heated radius calculation of multi-cycle had not been revolved. Three generalization mathematics models of the heated radius with multi-cycle were given on the basis of the three classical models. Different performance characteristics of heavy oil, extra heavy oil and super heavy oil are analyzed by means of the three generalized mathematical models and actual oil field data.
- North America > United States > Texas (0.46)
- Asia > China > Liaoning Province (0.35)
Abstract The present paper describes a technique for determination of the free water level (FWL) in low-permeability chalk reservoirs along slanted or horizontal wells from logged saturations and porosities. The calculation is done by utilizing existing empirical correlations for drainage and imbibition capillary pressure curves. For each logged saturation and corresponding porosity the FWL is determined so that the calculated saturation equals the logged value. The method takes into account possible imbibition, which results in a FWL that is shallower than the original paleo FWL. The method has been applied to field data from the North Sea and it is shown that it can capture a tilting water level along a horizontal well. Introduction Low-permeability chalk reservoirs are characterized by high capillary entry pressures and large oil/water transition zones. Estimation of oil-in-place for a reservoir at any given time requires information on the reservoir history, a good description of the capillary pressure behavior and knowledge of the FWL. The latter is typically non-horizontal and free water level gradients of more than 100 m/km have been observed in the North Sea. In general the information about the FWL is very scarce, and consists of sometimes dubious pressure measurements. If measured at all, it is usually done in a few exploration wells only. By means of the present method the FWL may be estimated from saturations and porosities logged in production wells. It is usually assumed that the reservoir is in drainage equilibrium. However, frequently observed residual oil zones indicate that water influx have affected the saturations. (See f. ex. Albrechtsen et al.1). The saturation distribution is a result of drainage as well as imbibition processes and its shape may deviate considerably from the shape of a drainage curve. The present method takes into account possible imbibition, which results in a FWL that is shallower than the original paleo FWL. Adams 2,3 presented an empirical model, the Imbibition from Drainage or IFD model, relating imbibition capillary pressure curves to the corresponding primary drainage curves. The IFD method was, however, developed for siliciclastic rocks in the Eromanga Basin in Australia and it appears to be less suited for tight chalk with high capillary entry pressures. A new technique was presented by Bech et al. 4 for the modeling of initial saturations along vertical wells in water wet oil/water reservoirs with large transition zones that are in imbibition equilibrium. The method determines the locations of the existing and the original free water levels and thus the extent of imbibition that the reservoir has undergone by matching calculated and log derived water saturations. The present paper extends this method to non-vertical wells. The capillary pressure curves including the curves representing the scanning between the drainage and imbibition curves are described by analytical expressions as shown in Skjaeveland et al.5. Optionally, the scanning may be described by Killoughs method 6. The drainage capillary pressure shape function, the irreducible water saturation and the capillary entry pressure are modeled by the EQR method 7. The method is demonstrated on a synthetic example and applied to field data from the North Sea and it is shown that it can capture a tilting water level along a horizontal well. Determination of Present Free Water Level The FWL is determined for each saturation and corresponding porosity logged along the well. It is assumed that the oil accumulation was originally formed through a pure drainage process resulting in a drainage equilibrium saturation distribution and a palaeo free water level. In case that subsequent burial and/or tectonic events have lead to an influx of water, the free water level has risen and a new saturation distribution has emerged in the reservoir. This new saturation distribution is a result of both drainage and imbibition processes and can therefore not be described by a model which assumes drainage equilibrium. The changes in water saturation and FWL is illustrated in Fig. 1 for a constant-porosity vertical well. It is seen that the oil-water contact is unaltered and that a region with residual oil has been left below the rising water table.
- Oceania > Australia (1.00)
- North America > United States (1.00)
- Europe > Denmark > North Sea > Danish Sector (0.29)
- Oceania > Australia > South Australia > Eromanga Basin (0.99)
- Oceania > Australia > Queensland > Eromanga Basin (0.99)
- Oceania > Australia > Northern Territory > Eromanga Basin (0.99)
- (7 more...)
Abstract Modern reservoir engineering practices require accurate information on thermodynamic and transport fluid properties together with reservoir rock properties to perform material balance calculations. These calculations lead to the estimation if initial hydrocarbons, the future reservoir performance, optimum production schemes and ultimate hydrocarbon recovery. These fluid properties which are usually determined by laboratory experiments or using empirically derived correlations provide the information required to properly understand the phase behavior, evaluate various production scenarios, optimize reservoir production and IOR schemes, and to maximize ultimate recovery and optimize production economics. One of these properties is the petroleum reservoir fluid viscosity. Crude oil viscosity is an important physical property that controls and influences the flow of oil through porous media and pipes. This paper introduces a new implementation of the genetic algorithms technology in petroleum engineering. Intelligent techniques such as genetic algorithms for data analysis and interpretation are an increasingly powerful and reliable tool for making breakthroughs in the science and engineering. The introduced model in this paper can predict the reservoir fluid viscosity data with genetic algorithms technique. Prediction results of the proposed model have been tested against the measured reservoir fluid viscosity data. Results indicate that the proposed prediction model can successfully predict and model reservoir fluid viscosity. Introduction Modern reservoir engineering practices require accurate information on thermodynamic and transport fluid properties together with reservoir rock properties to perform material balance calculations. These calculations lead to the determination (estimation) of the initial hydrocarbons in place, the future reservoir performance, optimal exploration and production schemes, and the ultimate hydrocarbon recovery. Reservoir simulators are routinely used to predict and optimize oil recovery from different fields. These softwares require input properties of the reservoir fluids as a function of pressure, temperature and composition and the accuracy of these input parameters can affect the results of the simulation. One of these parameters is the petroleum reservoir fluid viscosity. Crude oil viscosity is an important physical property that controls and influences the flow of oil through porous media and pipes. The viscosity, in general, is defined as the internal resistance of the fluid to flow. The oil viscosity is a strong function of the temperature, pressure, oil gravity, gas gravity and gas solubility. Whenever possible, oil viscosity should be determined by laboratory measurements at reservoir temperature and pressure. The viscosity is usually reported in standard PVT analyses. If such laboratory data are not available, engineers may refer to published correlations, which usually vary in complexity and accuracy depending upon the available data on the crude oil. The viscosity of crude oils is a critical property in predicting oil recovery. Viscosity reduction and thermal expansion are the key properties to increase productivity of heavy oils. Reservoir simulators are routinely used to predict and optimize oil recovery from oil fields. These simulators require as input properties of the reservoir fluids as a function of pressure, temperature and composition. The accuracy of the fluid properties can decisively affect the results of the simulation. Among the required fluid properties are phase densities, phase viscosities, formation volume factors and dissolved gas-oil ratios. The physicochemical properties of the reservoir fluids are a function of the fluids' composition. These compositions can be determined by experimental analysis such as, true boiling point essays and gas chromatography. In many practical cases no compositional information is present. A practical method to predict reservoir fluids' viscosities should be able to calculate viscosity of compositional and black oils. Numerous viscosity-correlation methods have been proposed. None, however, has been used as a standard method in the oil industry. Since the crude oil composition is complex and often undefined, many viscosity estimation methods are geographically dependent. Most correlation methods can be categorized either a black oil or as compositional.
- Geology > Geological Subdiscipline (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.54)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract Different sources can be used to develop rock strength information along the wellbore. Such strength information is important when assessing the stability of the wellbore, selecting mud weights and designing casing programs. However, there are other areas, especially in drilling, where rock strength information is applicable, but still underutilized. A methodology is developed to estimate drilling time and bit wear before drilling if rock strength is known. To estimate drilling time and bit wear, effects of other parameters like drilling parameters, well bore size and drilling bit design has to be normalized. This methodology has been used to estimate drilling time and estimate bit wear and further evaluate drilling performance while drilling. After drilling the additional information has been used to conduct a post analysis and transfer knowledge from well to well. The advantage of this methodology is it eliminates the effect of geological variability when comparing performance between wells and fields. Introduction Various methods can be used to develop rock strength profiles along the wellbore. Such strength information is crucial when analyzing the safe mud weight window for assessing the stability of the wellbore, selecting mud weights and designing casing programs. Strength information is also used for completion and hydraulic stimulation design. However, there are other areas, especially in drilling, where rock strength information is applicable, but still underutilized. To obtain the rock strength along the well bore, logs, rock mechanical tests or even drilling data can be used. In this paper we address how to obtain this rock strength and some areas where rock strength has been provided to give valuable information for drilling purposes. Rock strength calculations The rock mechanical parameter that is most important when conducting drilling analysis is unconfined compressive rock strength (UCS)1. The UCS can be determined from Mohr Coulomb failure criteria. The Mohr-Coulomb failure criterion in terms of peak loads is given as: Equation (1) Where S'v is vertical effective stress, S'h is horizontal effective stress, and a is failure angle. Effective stresses are defined the difference between total stresses and pore pressure. Equation (2) Sv is the total stress, pp is the pore pressure. There are several methods to obtain UCS along the well bore. In most cases, the availability of data determines which methods to choose. Different methods for obtaining UCS are described below.
- North America > United States (0.69)
- North America > Canada (0.48)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.32)
Abstract The Bati Raman field is the largest oil field in Turkey and contains some 1.85 billion barrels of oil initially in place. The oil is heavy (12 oAPI) with high viscosity and low solution gas. Primary recovery has been inefficient, less than 2% of OOIP. Over the period of primary recovery, from 1961 to 1986, the reservoir underwent extensive pressure depletion from 1,800 psig to as low as 400 psig in some regions, with a related production decline from a peak of approximately 9,000 Bbls/day to 1600 Bbls/day. In March 1986, a CO2 injection pilot scheme in a 1200 acre area containing 33 wells was initiated in the west portion of the field. The gas injection was initially cyclic; "huff and puff" method was applied. Later, in 1988, the gas injection scheme was converted to a CO2 flood process. Later, the process was widespread to cover the whole field. A peak daily production rate 13000 STB/d was achieved in 1993 in comparison to what would have been less than 1600 STB/d without CO2 application. However, since 1995, the field has undergone a progressive production decline to recent levels at approximately 5,500 Bbls/day. Polymer gel treatments were carried out to increase the CO2 sweep efficiency and arrest the decline. Multilateral and horizontal well technology was also applied on pilot scale to reach the bypassed oil. WAG is applied widespread now. Current production is 7000 Bbls/day. This paper documents TPAO's 25+ years of experience on the design and operation of full field immiscible CO2 injection recovery project conducted in the B.Raman heavy oil field, in Turkey. The objective is to give an up-to-date status of the performance of the application; reservoir/field problems that TPAO had, unexpected occurrences and results and just a general idea of how successful the project has been. Introduction The Bati Raman field, which is the known highest oil accumulation in Turkey, contains very viscous and low gravity oil in a very challenging geological environment. Due to the fact that the recovery factor by primary recovery was limited, several EOR techniques had been proposed and tested in pilot level in the 70s and 80s. Based on the success in the lab tests and vast amount of CO2 available in a neighboring field which is just 55 miles away from the Bati Raman field, field scale huff-and puff injection was started in the early 80s. Due to the early breakthrough of CO2 in offset wells in a short period of time, the project was converted to field scale random pattern continuous injection. Over more than 20 years of injection, the recovery peaked at ∼13,000 bbls and began to decline reaching today's ∼7,000 bbl value. In the case of Bati Raman, at this mature state of the process, the injected agent is increasingly bypassing the remaining oil and production is curtailed by excessive high gas oil ratios (GOR). The naturally fractured characteristics of the reservoir rock has been a challenge for establishing a successful 3D conformance from the beginning and its impact is even more pronounced in the later stages of the process. Because of that reason, the subject field requires modification on the reservoir management scheme to improve recovery factors as well as improving productivity of the current wells. BATI RAMAN FIELD Bati Raman was discovered in 1961 in Southeastern Turkey with the completion of BR-1 (Fig-l). The producing formation is the Garzan Limestone, a very heterogeneous carbonate of Cretaceous age. The reservoir fluid is a very heavy crude oil, having an API gravity ranging from 9.7 to 15.1 and a viscosity ranging from 450 to 1000 centipoises at reservoir conditions. The structural trap is a long; partly asymmetric anticline oriented in the east-west direction which measures about 17 km. long and 2 to 4 km. wide. It is limited by an oil/water contact at 600 meters subsea in the north and west, by a fault system in the southwest and south, and by a permeability barrier in the southern and southeastern part of the field. Formation has a gross thickness of 210 ft (64m). The oil column from the top of structure to the OWC is about 690 ft.
- North America > United States > Texas > Permian Basin > SACROC Unit > Lower Clear Fork Formation (0.99)
- North America > United States > Texas > Permian Basin > SACROC Unit > Cisco Sand Formation (0.99)
- North America > United States > Texas > Permian Basin > SACROC Unit > Canyon Reef Formation (0.99)
- (6 more...)
Successful Experiences for Water and Gas Shutoff Treatments in North Monagas, Venezuela
Perdomo, Lenin (Servicios y Suministros de Oriente) | Rodriguez, Hector A. (Petroleos de Venezuela S.A.) | Llamedo, Maria Asuncion (PDVSA Intevep) | Oliveros, Luis (Petroleos de Venezuela S.A.) | Gonzalez, Edwin Rafael (PDVSA Intevep) | Molina, Osmel (Petroleos de Venezuela S.A.) | Giovingo, Claudio (Servicios y Suministros de Orien)
Abstract Normally, the average on success rate of water shut-off treatments in the world ranges between 40% - 50%. This average is lower for gas shut-off treatments. In North Monagas, Venezuela, where some hard conditions are present, such as high bottom hole temperatures (over 280 F), in some cases very tight reservoirs which complicates a matrix penetration of any gel treatment without overcoming the fracture gradient, in other cases naturally fractured wells which complicates the effectively fillout of all fracture nets; this success rate could decrease even more, making the treatment not economically attractive. In order to obtain a high success rate in North Monagas, it was designed a special aqueous polymer gel with a low viscosity to handle the difficulty of matrix penetration without overcome the fracture gradient, and delay its crosslinking process for a period of long time due to very low injected rates. Also, it can maintain its blocking properties at temperatures over 290 F without the need to inject cool-down pads; it can perform effectively even gas shut-off treatments, taking into account its difficulty due to higher mobility than water and oil. Carefully studied Coiled Tubing (CT) operations were carried out taking into account the hard conditions already described, without damage other oil producing zones. Several case histories are showed in this paper, including some gas shut-off operations, with the results obtained in each one. As a result, higher water/gas shut-off success rate than the typical average (40% - 50%) was achieved. Introduction Some of the most important reservoirs in Venezuela are located in North Monagas area, which production started in 1988, by PDVSA (Venezuelan National Oil Company). It is placed at Eastern Venezuela (Fig 01). This area is divided in 5 fields called: El Furrial, Orocual, Jusepin, Carito and Pirital. In terms of overall production, El Furrial, Carito and Pirital exhibit the highest production of the area. All presented cases are related to these three fields. North Monagas overall production is about 860,000 BOPD. North Monagas area shows a compositional gradient fluid system that varies from gas condensate to medium oil. Oil API gravity vary with depth from 40 to 16. It is characterized as follows: Vertical or low slanted wells (less than 30 degrees), with a few exceptions. Depths range from 14,000 ft to 19,000 ft. Bottom Hole Static Pressures (BHSP) range from 6,000 psi to 8,500 psi. Bottom Hole Static Temperatures (BHST) range from 280 to 310 F Absolute Permeabilities (K) vary from 20 to 1800 md Average Production per well ranges from 500 BOPD to 5,000 BOPD, with a few wells with higher production. Wells on this area are completed with cemented casing, and then perforated. Wells flow spontaneously, without any artificial lift method. Production problems are related to asphaltene precipitates and unwanted gas/water production, and in other few cases with sand production. Coiled Tubing (CT) operations are very often in order to maintain production. Well Details Different well completions on this area are described as follows: 5−/2″ monobore wells (5−1/2″ tubing and production liner). 4−1/2″ and 3−1/2″ tubing with 7″ or 5−1/2″ production liners. Double strings wells (3−1/2″ plus 2−7/8″ tubings) with 7″ production liners. Wells analyzed in this document are completed with the first two types in the list above (one single production string). Figure 02 shows a typical well schematic from the area. In this case, the well has a 4−1/2″ production tubing and a 5−1/2″ production liner.
- South America > Venezuela (1.00)
- North America > United States > Texas (1.00)
- Asia > Middle East > Qatar > Arabian Gulf (0.24)
- South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Santa Barbara Field (0.99)
- South America > Venezuela > Caribbean Sea > Venezuela Basin (0.99)
- South America > Venezuela > Monagas > Eastern Venezuela Basin > Maturin Basin > Carito Field (0.93)
- (2 more...)
Abstract Upscaling reservoir properties for reservoir simulation is one of the most important steps in the workflow for building reservoir models. Upscaling allows taking high-resolution geostatistical models (107–108 grid blocks) to coarse scale models (104–105 grid blocks), manageable for reservoir simulation, while retaining the geological realism and thus effectively representing fluid transport in the reservoir 1,2. This work presents a study of the effectiveness of different available techniques for permeability upscaling and the implementation of a new technique for upscaling of relative permeability curves based on the numerical solution of a two-phase system and the Kyte and Berry method3. The reference fine scale model considered in this study is a conceptual fluvial reservoir based on the Stanford V model4. The reference fine scale isotropic and locally heterogeneous permeability distribution was upscaled to different upscaling ratios by means of analytical (static) and numerical single-phase (pressure solver, dynamic) techniques. Two-phase flow simulations were performed on the reference fine grid and upscaled models using a comercial black-oil simulator. Arithmetic, harmonic, and geometric averages were defined for static upscaling of the permeability distribution. The dynamic upscaling process considered one-phase and two-phase upscaling. One-phase upscaling considered upscaling of the permeability distribution and two-phase upscaling considered upscaling of the permeability distribution and relative permeability curves. Flow simulation results for waterflooding in the coarse scale model indicated relevant discrepancies with the fine grid results. Compared to fine-scale, flow results of the single-phase upscaling process indicated that the coarsest upscaled models did not match the water breakthrough times, water cut values, or well pressures from the reference model. The finer upscaled models reproduced the reference results more accurately than the coarser models. The two-phase dynamic upscaling technique implemented in this work resulted in the best match with the flow simulation results of the fine grid model. Results show that the most accurate upscaling scheme should be defined using the two-phase dynamic upscaling technique on the model with the smallest upscaling ratio. Introduction A geological model generated using geostatistical techniques often can contain detailed geologic information in multiple directions and at different scales. The detailed geologic information can be comprised of varying degrees of heterogeneity, anisotropy, or different length scales. As much detail as possible is desired to make an accurate and precise geologic model. Such an accurate and precise geologic model is capable of characterizing reservoirs accurately in terms of compartmentalization, connectivity, and structure. In terms of computer memory and storage, however, more detail means models of larger sizes, on the order of 107 to 108 cells. Although an accurate and well-characterized reservoir is desired, the complexity, and thus the size of the model, can introduce significant computational problems when performing reservoir simulations. An effective way of upscaling is requiered, which reduces the CPU demand and run time while preserving the geology, especially the important flow features such high permeability zones.
- South America (0.69)
- North America > United States > Texas (0.28)
- Research Report > New Finding (0.54)
- Overview > Innovation (0.34)
Abstract The objective of this study is to investigate the advantages of drilling horizontal wells on oil recovery improvement. To evaluate the effect of horizontal length, porosity, anisotropy, staggeredline well pattern with gas injection (in the top layer) and water injection (in the bottom layer), different scenarios were studied. This study is focused on simulation of formation-A of a carbonate reservoir which consists of three layers; where horizontal well is going to be drilled in layer-2 of this formation. The average thickness of formation-A is about 167.64 meters (550 feet) and we also have tilted water oil contact in this formation. This field has produced around 146.9 MMBBL until year 2002 for the last 12 years with the Recoverable Reserve of about 237.31 MMBBL. Up to now 40 wells have been drilled in this field. IRAP/RMS software was used to generate geological model. Based on selected reservoir black-oil model, IMEX from CMG is used for simulation task. Sector model used for simulation as we only had one productive horizontal well in the formation (scale-down method). This study confirms simultaneous use of overbalance method and horizontal well in this reservoir in order to:Increase production rate up to 3 to 4.5 times by boosting productivity index (PI). Communicate a large area leading to a better drainage area. Postpone the water breakthrough by minimizing the draw down pressure. Introduction To identify the key factors controlling the impact of drilling new horizontal well at the reservoir scale are always a fundamental issue. Once this identification is done, simulation model will allow determination of which combination of vertical and horizontal wells will be the most suitable drilling activity in order to enhance the production. The impact of a horizontal well on the reservoir will depend on many factors. This includes the number of existing wells, well spacing, formation thickness, Kv/Kh, type of drive mechanism, completion intervals of vertical wells, well radius, drainage radius, oil viscosity (and other PVT properties) and obviously the length and placement of horizontal wells. The aim of this study is to assess effect of horizontal well performance on boosting oil recovery. A case study from one of Iranian reservoirs is simulated.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.69)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.47)
- Geology > Petroleum Play Type > Unconventional Play > Heavy Oil Play (0.47)
- Europe > Hungary > Algyo Field (0.99)
- Asia > China > South China Sea > Beibu Gulf Basin > Beibu Gulf > Weizhou Field (0.98)
Abstract The Beta distribution in n-dimensions is introduced to describe the proportions of the mineralogical components existing in a certain stratigraphic interval (the porosity is included as a "mineralogical component"). The justification for doing so is empirical. The model allows the calculation of well logging parameters, such as GRma, GRsh, shale density, etc., without having to introduce them by "eye". It also allows the probabilistic calculation of the rock composition at each depth when there are more mineralogical components than logs: that is, there is a shortage of equations. In addition to this, the Beta model can be used to test the hypothesis that the relationship between any two components can be regarded as random, which should have applications in reservoir characterization. Introduction Sedimentary rocks may be ultimately described as a mixture of minerals and pores. For a given lithological column, it is possible to calculate the composition of the rocks at discrete points, with well logs. We may ask which should be the probability distribution of the volume fraction of each mineral component (with the porosity included as a "mineral component") along this lithological column. This distibution should satisfy at least the following conditions: The values of each of the components should range between 0 and 1 The sum of all the components should be equal to 1, for all points. The well known Beta distribution, which is also known as the Dirichlet distribution (en.wikipedia.org/wiki/Dirichlet_distribution) in the multidimensional case, satisfies these requirements. Although, in theory, this distribution allows for a porosity of 1, in practice the values of the parameters of the distribution are such that very high porosities are extremely unlikely. There are also empirical observations which support the use of this distribution to model rocks. It is quite frequent to see histograms of the Gamma Ray log over more or less "homogeneous" intervals, which are clearly unimodal and asymmetrical (i.e. they are skewed). If we assume that the Gamma Ray log is sensitive to only one component (the "shale"), then, if the shale volume fraction is Beta distributed, the character of the Gamma Ray log can easily be explained. In summary, despite the lack of a sound theoretical background, there are some numerical characteristics and empirical observations which justify the introduction of this distribution. There are two main reasons to use this distribution in petrophysics and well log analysis: It allows the calculation of parameters from the data, without having to introduce them arbitrarily or by "eye" (for instance, the estimation of Grma, the Gamma Ray response of the "clean" rock and Grsh, the Gamma Ray log response of the "pure" shale). In zones of complex mineralogy, where there are more components than logs, the Beta model allows the introduction of further equations which ultimately result in a solution - albeit a probabilistic one - of the system. This problem has also been dealt with by McCammon (1972), although the approach of this author is quite different, applying Information Theory to solve for the proportions of the components. In this paper, we will calculate Grma and Grsh from the Beta model for some real cases. The second point we will deal with from a theoretical point of view. In addition to these practical applications, the theoretical question of whether or not sedimentary rocks can be regarded as random mixtures of components will be considered. Properties of the Beta Distribution To simplify the problem, we will restrict our discussion to mixtures of three components (say "sand","shale" and effective porosity). However, the principles discussed below are easily extended to mixtures of n components.
Abstract This paper describes an implementation of method to optimize the production in intelligent wells varying the wells inflow control valves settings using an optimization algorithm coupled to commercial flow simulators. The optimization is based on direct search methods. The optimization algorithm was coupled with two different commercial flow simulators and has been applied in two real Brazilian offshore fields to quantify the benefits of intelligent wells over a base case with conventional completion. The first field has three horizontal wells, two producers and one water injector, completed in two zones totalizing six inflow control devices. In this case, different scenarios have been analyzed varying the downhole valves type - on-off and multi-position. The results have shown that the intelligent wells scenarios increased the recovery factor and reduced the production and injection of water when compared with the base case (conventional completion). The second field has fifteen wells - nine producers with binary valves and six water injectors with six-position valves - producing and injecting in two or three zone totalizing 39 downhole valves to be optimized. In this case, the results have shown a significant increase of the expected cumulative oil production when compared with the base case. Introduction The intelligent well technology provides the capability to remotely monitor and manage multiple production zones independently, reducing the cost of wells interventions, accelerating the production and reducing the injection and production of water. The ability to control multiple production zones comes from downhole inflow control valves. These devices may be binary (on-off behavior), or multi-position, choking the production zone with a discrete number of positions, or infinity variable position. The benefits of the intelligent wells technology were shown in practical applications 1–6 especially for multiple-zone producing commingled. During the operation with intelligent wells, one possible approach is to react when problems occur, for instance, choke the production zones with high water cut. Yeten et al.7 has called this approach as reactive control strategy. Another approach is to use the intelligent completions in conjunction with a predictive reservoir model. This model may be coupled with optimization algorithms to define production strategies that maximize the value of the field. Some previous authors have studied the production optimization with intelligent wells. Brouwer et al.8, have presented a methodology that maximized sweep in a water flood study. The strategy was based on choking the segments with highest productivity index and redistributing the production in others segments. Brouwer et al.9, have applied the optimal control theory for production optimization. Yeten et al.7 have used a conjugate gradient optimization method coupled with a reservoir simulator to optimize the production with intelligent wells. They have proposed to divide the simulation into several steps of optimization. Ajayi et al.10 have applied an optimization process based on derivatives calculated as the change of the production rate of undesired reservoir fluid, water or gas, with the correspondent change in the desired fluid, oil or gas. The process corresponds to choke the zone with highest derivatives values in each time step. Naus et al.11 have proposed an optimization strategy with infinitely variable inflow control valves using a sequential linear programming to maximize production at a specific moment in time. This paper presents an implementation of method to optimize the production in intelligent wells varying the wells inflow control valves settings using an optimization algorithm coupled to commercial flow simulators. The optimization is based on direct search methods. This kind of algorithm has some advantages in this case: the algorithm is based only in objective functions evaluations, this fact allows to consider the flow simulator as an external program like a "black box"; the algorithm permits to model binary and multi-position downhole valves, differently of gradient-based algorithms where is difficult to model problems with a finite number of discrete solutions; and the algorithm takes advantage in a grid computer environment, because the objective function evaluations can easily be done in parallel.
- Europe (0.96)
- North America > United States > Texas (0.69)
- South America (0.69)
- South America > Brazil > Rio Grande do Norte > South Atlantic Ocean > Potiguar Basin (0.99)
- South America > Brazil > Campos Basin (0.99)