At Kuwait Oil Company (KOC) most of the ESP wells are running with downhole sensors to enhance the daily monitoring routine and for having a better knowledge of the pumps performances. However, one of the most important parameter of these ESP Wells is only known after a time period within 3-6 months: The Flow Rate. Production Tests are obtained using Multiphase Flow Testing Units which usually last between 4 and 6 hours that are also utilized to conduct some sensitivities such as choke size and motor speed changes. At Well Surveillance Group, a tailored fit model was developed from which the ESP flow rate can be estimated based on the downhole sensor data and basic fluid properties with an overall deviation below 2% (when they are compared to the results obtained from the Testing Unit). In this sense, flow rate monitoring can be performed at any time and flow testing time and associated cost can be reduced among other benefits. The method requires knowing the ESP model and total number of stages installed in the well, and then using the corresponding performance curve of the ESP model usually provided by the manufacturer, the data is processed and the calculation performed. This work aims to show how this model works, advantages, limitations, implementation status and future improvements.
Significant advances have been made in formation testing since the introduction of wireline pumpout testers (WLPT), particularly with respect to downhole fluid compositional measurements. Optical sensors and the use of spectroscopic methods have been developed to improve sample quality and minimize sampling time in downhole environments. As a laboratory technique, spectroscopy is a ubiquitous and powerful technology that has been used worldwide for decades to measure the physical and chemical properties of many materials, including petroleum, geological, and hydrological samples. However, laboratory-grade, high-resolution spectrometers are incompatible with the hostile environments encountered downhole, at wellheads, and on pipelines. Only limited resolution techniques are available for the rugged conditions of the oil field. This paper introduces a new optical technology that can provide high-resolution, laboratory-quality analyses in harsh oilfield environments.
A new technology for optical sensing, multivariate optical computing (MOC), has been developed and is a non-spectroscopic technique. This new sensing method uses an integrated computation element (ICE) to combine the power and accuracy of high-resolution, laboratory-quality spectrometers with the ruggedness and simplicity of photometers. Many modern sensors typically merge the sensor with the electronics on an integrated computing chip to perform complex computations, resulting in an elegant yet simplistic design. Now, optical sensing using ICE features an analogue optical computation device to provide a direct, simple, and powerful mathematical computation on the optical information, completely within the optical domain. Because the entire optical range of interest is used without dispersing the light spectrum, the measurements are obtained instantly and rival laboratory-quality results.
A proof of concept MOC with ICE has been demonstrated, logging more than 7,000 hours, in nearly continuous use for 14 months. Oils with gravities ranging from 14 to 65°API have been measured in downhole environments that range from 3,000 to 20,000 psi, and from 150 to 350°F. Hydrocarbon composition measurements, including saturates, aromatics, resins, asphaltenes, methane, and ethane, have been demonstrated using the MOC configuration. As compositional calculations therein, GOR and density are validated to within 14 scf/bbl and 1%, respectively. The paper discusses the details of the new ICE-based sensor and describes its adaptations to downhole applications.
Copyright 2012, Offshore Technology Conference This paper was prepared for presentation at the Arctic Technology Conference held in Houston, Texas, USA, 3-5 December 2012. This paper was selected for presentation by an ATC program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Offshore Technology Conference and are subject to correction by the author(s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of OTC copyright.
Methodologies and numerical tools are available (1) to construct geologically realistic models of fracture networks and (2) to turn these models into simplified conceptual models usable for fieldscale simulations of multiphase production methods. A critical step remains however, that of characterizing the flow properties of the geological fracture network. The multiscale nature of fracture networks and the associated modeling cost impose a scale-dependent characterization: (1) multiscale fractures that may be characterized in local dynamic test areas, e.g., drainage areas involved in well tests, through the calibration of geologically realistic fracture models; and (2) large-scale faults that are characterized through reservoir-scale production history simulations that involve upscaled flow models with an explicit fault representation. However, field data are commonly insufficient to fully characterize the multiscale fracture properties. Therefore, efficient inversion methodologies are necessary to sample wide ranges of property values and to characterize a variety of solutions, i.e., fracture models that are consistent with dynamic data. This article presents an inversion methodology to facilitate the characterization of fracture properties from well-test data. A genetic optimization algorithm has been developed and coupled with a three-dimensional fracture model upscaling simulator to perform the simultaneous calibration of well-test data, i.e. equivalent transmissivities K·h, with K the equivalent permeability that takes into account fracture flow properties, and h the reservoir thickness over which the well test has been interpreted. Several genetic crossover and mutation strategies were studied and tested on three geologically realistic fractured reservoir models, involving both small-scale diffuse fractures and large-scale sub-seismic faults. The characterized diffuse fracture properties are mean length, mean conductivity, orientation dispersion factors, and facies-dependent properties such as fracture density. The fault network conductivity is also characterized. The effectiveness of this inversion methodology to characterize physically meaningful and data-consistent fracture properties is discussed.
Inverse problem, genetic algorithm, KH calibration, fracture, characterization
Geological models are designed from the integration of various type of data (seismic data, well data, outcrop observations...) . These data are usually sparse, scarce or scale-limited, i.e. they do not allow one to fully characterize the geological features in a given area of interest. Therefore geological models also result from many assumptions made on various parameters distributions, with associated space-variable uncertainties. To reduce these uncertainties, flow-related data are also used, geologically-constrained flow models are designed and flow simulations are performed . Uncertain parameters are characterized via calibration of the simulated results with flow-related data. The calibrated flow models are then used for estimating the reservoir production capacity and developing strategies for optimizing the production  In particular, fractured reservoirs are difficult to characterize, mostly due to the multiscaled character of fracture network and their high degree of heterogeneity . Specific workflow and tools have been developed for fractured reservoir characterization in the past years . Although some progress have been made in fracture detection, the presence of fractures at various scales affects the flow dynamics within a reservoir, and casts large uncertainties on the assessment of the field productivity and reserves. This paper focuses on the characterization of fracture properties from well tests. Well tests are perturbations of well rates over a given period of time, during which the well pressure response is recorded. The inverse problem is then to infer the fracture properties from well tests pressure measurements.
Summary This paper details an alternative method to the commonly used geostatistical simulation approaches for uncertainty analysis, which are generally time consuming and do not give access to uncertainties associated to calibration sets. The proposed approach uses a deterministic route to evaluate inversion uncertainties and then propagates them into the seismic characterization workflow in order to predict jointly properties and associated uncertainties. In practice it associates a Bayesian inversion method to estimate elastic parameters and a "bootstrap" method for property estimation and uncertainty assessment. Such approach appears to be particularly well adapted for the case study presented, as the main source of uncertainty is related to the calibration set: limited number of calibration samples and uncertain seismic attributes (impedance from inversion). The method allows assessing the uncertainties while turning the attributes into reservoir properties, as well as propagating the uncertainties attached to the attributes in the interpretation process.
Multi-stage fractured horizontal wells (MFHW) in unconventional resource plays often present formidable reservoir management challenges, particularly with regard to capital utilization and allocation. In this study, well performance histories of some 74 wells in the Montney siltstone play were investigated with a common and consistent analytical framework. Parameters determined from the analyses are key indicators of the combined result of reservoir quality and hydraulic fracture performance (subsurface and completions). The analytical approach utilized in this study was then used to provide robust physics-based forecasts that directly recognize and incorporate interpretation non-uniqueness. Through a forecasting regimen that explicitly provides expected ranges of results, insights and conclusions in field optimization, well spacing and completions design have been drawn.
A real distribution of well productivity and predicted recovery enabled identification of "sweet spots??. Openhole completions technique did not show poorer performance compared to limited entry style completions, though further evaluation and surveillance would seem warranted. Wells that were flowing under a "high-drawdown?? showed a lower productivity, higher completion resistance (skin) to flow, and lowest predicted final recovery.
Wells completed at 50 m fracture spacing and 30 tonnes of proppant per cluster performed similarly to 100 m spacing and 60 tonnes per cluster, suggesting no apparent difference in capital efficiency between these two completion styles. Results indicate that the frac half lengths in the 50 m cluster spacing wells are shorter compared to wells with 100 m cluster spacing (based on the reduction in the amount of proppant pumped per cluster). Trends of estimated original gas-in-place inside the SRV and predicted 30-year recovery for wells drilled at close well spacings, (closer than 400 m between wellbores) indicate effects of inter-well interference. Performance of wells at 200 m well spacing seem to be affected the most by inter-well interference.
A consistent workflow for analyzing well performance and predicting future performance of MFHW in unconventional gas wells is presented that provides a means to assess the impact of business and development decisions and determining practices worth replicating across the Montney play.