Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. A methodology that allows the recognition of well-calibrated models within an acceptable deviation is used. This procedure helps to identify the critical uncertain parameters and their possible variation in order to estimate the representative reserve range. The goal is not to find the best deterministic match, but rather to show how the calibration process allows a mitigation of identified uncertainties.A real case based on a reservoir from Campos Basin in Brazil was used. A 14 year historical period followed by a 12 year forecast period was considered, allowing verification and validation, at a global level, of the proposed procedure in a complex dynamic model. Two different commercial softwares were used, in order to demonstrate the advantages and restrictions of each approach. Distribution variations of the responses in time were evaluated by Latin Hypercube sampling and Monte Carlo propagation on validated proxy models.The proposed methodology allows: (1) to reduce the range of possible models taking into account the observed data;(2) to identify the existent uncertainty as a function of observed data; (3) to reduce the uncertainty range of critical reservoir parameters; (4) to increase confidence in production forecast. One contribution of this work is to present a quantitative approach for increasing the reliability of reservoir simulation as an auxiliary tool in decision making processes in order to reduce the associates risk and to maximize development opportunities. IntroductionThe field used for this work is located on Campos Basin in Brazil, about 80 km from the shore. The field is constituted from siliciclastic turbidite reservoirs under a water depth between 300 and 800 m. The main sand reservoir has good petrophysical characteristics (roughly 27% porosity and 3000 mD permeability) and also good-quality oil (29° API and 2.1 cp viscosity).The field has a high sand / shale ratio and several normal faults, resulting in blocks with good hydraulic communication. The main production block is divided in three stratigraphic zones...

The use of uncertainty analysis as a tool in reservoir studies is becoming more and more common inside Petrobras and all around the world. However, in fields with production history, traditional uncertainty analysis, combining possible values of uncertainty variables, can lead to models that poorly represent the reservoir and to results that do not respect the available dynamic data. During uncertainty analysis process, history matching evaluation can considerably reduce the existing uncertainties.The methodology used in this work is based on experimental design and response surfaces. Besides the cumulative production response surface, another one is generated to represent the quality of the history matching. Only cases with a good history matching are selected as input to the Montecarlo simulation. With this technique, it is possible to evaluate the initially defined probability distributions and, if necessary, to redefine shape or limits for the probability density curve.The methodology was applied in a real study in Petrobras. There are uncertainties related to faults, absolute permeability and also related to the existing fluid properties. Although there are other wells in the same block, the studied area is located in a sea-bottom slope region, where water depth varies considerably within the block, possibly influencing the oil quality.Since there are two wells operating in the studied region, one producer and one injector, the developed analysis took the existing dynamic data into account, reducing model uncertainties. IntroductionThe field in study is located in Campos Basin. It comprises around 150 km 2 in area and the water depth varies from 800 m to 2000 m. There are zones in production of different geological ages, from Oligocene to Miocene.The development project contains around 30 wells, comprising producers and injectors. The well location plan was developed in order to maximize the drainage of the several compartments, minimizing gas-oil ratio and to postpone water breakthrough.After a few years in operation, with the production decline, some new locations have been studied in order to take advantage of the remaining platform capacity. The target study is located in a region with uncertainties related to faults existence, rock permeability and also related to fluid properties. Although there are drilled wells in this same block, the aiming area is located in a sea bottom slope region, so the water depth varies considerably along the block, possibly influencing the oil quality.In the studied region, there are two operating wells (a producer and an injector well), both horizontals. Once production history exists in this block, the analysis took in account the existing dynamic data. Traditional uncertainty analysis, combining possible values of uncertainty variables, can lead to models that poorly represent the reservoir and to results that do not respect the available dynamic data. During uncertainty analysis process, history matching evaluation can considerably reduce the existing uncertainties.

The objective of this study is to determine the minimal well length required to achieve a desired productivity index (PI). It considers the main uncertainties associated to fluids and reservoir properties (vertical and horizontal permeability, net oil thickness and oil viscosity). Monte Carlo analysis is used to consider possible combinations of these parameters and generate probabilistic results. This study was developed for a heavy oil reservoir. Oil of 15ºAPI or less and viscosities up to 150 cp are expected. The results obtained can be used in the planning phase. The reservoir properties are evaluated initially by a pilot well; afterwards they are estimated along the horizontal length. During the horizontal well drilling, this model can be easily updated. A theoretical model presented in JOSHI (1988) is used to calculate the horizontal well PI. It considers the influence of anisotropy in permeability. This work is divided in two parts. Initially, a sensibility analysis is performed regarding each uncertainty parameter separately. This first stage is necessary to evaluate the most impacting parameters. The procedure was applied for several well lengths. In a second phase, Monte Carlo analysis is applied, considering simultaneously the uncertainties associated to these parameters. This analysis provided three levels for well PI: pessimistic, most probable and optimistic curves as a function of well length. This methodology is flexible and, for this practical case, it was implemented through a spreadsheet that comprised the required probability density functions and the Monte Carlo analysis. It can be implemented with other development programs that suit the reservoir engineer. The results obtained can improve the estimates for the performance of the wells and can be used to design adequate horizontal wells for field development. Introduction This paper presents the key aspects of the study of determining the minimal well length in order to provide a desired productivity index (PI) as well as the strategy adopted in order to overcome the main reservoir uncertainties. There are several uncertainties involved in the prediction of a well productivity index. These uncertainties are present in rock properties, like net oil thickness and absolute permeabilities, in fluid properties, like viscosity, and properties depending on both, like relative permeabilities. In a horizontal well, vertical permeability is also an important factor to estimate the productivity. In order to model the reservoir behavior, a good estimate of well PI is necessary. Depending on its value, the initial oil rate can be considerably different. This effect is more important when the PI values are low, and it is the case of heavy oil fields. For this case, due to the challenging field environment - low API and viscous oil, and reservoir thickness ranging from 15 to 35 meters - the use of emerging technologies such as long horizontal wells and thermal insulated flow lines is required. The field is located at water depths ranging from 800 m to 2000 m, with small sediment cover of only 500 m in the studied area. The reservoir is Miocene sandstone with high porosity and permeability. The fluid distribution in the reservoir is quite complex. The field development comprises 17 production and 15 injection horizontal wells and the oil production is around 27.000 m3/d (170.000 bpd), with 14 production wells operating. Methodology In a horizontal well, several aspects must be considered to calculate the productivity. A methodology was presented by JOSHI (1988), considering several aspects involved in the problem, like well eccentricity and anisotropy influence in permeability. In the present study, skin factor and horizontal well eccentricity were not considered. Although there are several methods for predicting horizontal well productivity index (GIGER, 1983, KARCHER et al., 1986 etc.), JOSHI equation was adopted since its results represent better the values found in other wells in the same field.