Updating Reservoir Models Research Articles

Assimilation of Geophysics-Derived Spatial Data for Model Calibration in Geologic CO2 Sequestration

Summary Uncertainty in geological models usually leads to large uncertainty in the predictions of risk-related system properties and/or risk metrics (e.g., CO2 plumes and CO2/brine leakage rates) at a geologic CO2 storage site. Different types of data (e.g., point measurements from monitoring wells and spatial data from 4D seismic surveys) can be leveraged or assimilated to reduce the risk predictions. In this work, we develop a novel framework for spatial data assimilation and risk forecasting. Under the U.S. Department of Energy’s National Risk Assessment Partnership (NRAP), we have developed a framework using an ensemble-based data assimilation approach for spatial data assimilation and forecasting. In particular, we took CO2 saturation maps interpreted from 4D seismic surveys as inputs for spatial data assimilation. Three seismic surveys at Years 1, 3, and 5 were considered in this study. Accordingly, three saturation maps were generated for data assimilation. The impact from the level of data noise was also investigated in this work. Our results show increased similarity between the updated reservoir models and the “ground-truth” model with the increased number of seismic surveys. Predictive accuracy in CO2 saturation plume increases with the increased number of seismic surveys as well. We also observed that with the increase in the level of data noise from 1% to 10%, the difference between the updated models and the ground truth does not increase significantly. Similar observations were made for the prediction of CO2 plume distribution at the end of the CO2 injection period by increasing the data noise.

Applying hydrodynamic principles and flow theory to optimize the reservoir pressure survey program without shut-in the well - Case study: Biba reservoir, Algeria

The article focuses on the importance of gathering and analyzing information related to oil and gas reservoirs, particularly parameters such as permeability (k), skin factor (S), pressure (p), and temperature (T). This data is crucial for making significant decisions in adjusting production technologies, intervening in wells, and near-well bore treatment. To obtain this information, analyzing data from tests is essential. Pressure, flow rate, and time data are collected using pressure buildup testing, where wells are produced at a constant rate and then shut-in to observe pressure increase over time. Analyzing this data allows evaluation of permeability, pressure in the drainage region, skin factor, and other parameters like static pressure. The article also discusses applying principles of hydrodynamics and flow theory to optimize well pressure survey programs without disrupting production operations. This method enables the collection of necessary information while maintaining continuous field operations and providing comprehensive technological data. The results were applied on Bali reservoir in Algeria. Information gathered from pressure survey programs not only helps fine-tune and optimize daily production procedures but also significantly contributes to updating reservoir models. This serves as a basis for future development decisions within the reservoir. The method has been successfully applied at Biba field, Algeria.

Data Assimilation of Production and Multiple 4D Seismic Acquisitions in a Deepwater Field Using Ensemble Smoother with Multiple Data Assimilation

Summary In recent years, time-lapse (4D) seismic (4DS) data have been widely used for reservoir monitoring to provide relevant information on dynamic changes occurring during production. In complex reservoirs, multiple seismic monitor surveys are usually available. Updating reservoir models with these data can be very beneficial to improve the field’s management. In the quantitative integration of 4DS data into the data assimilation (DA) process, it is crucial to define how to deal with more than one seismic monitor. In this work, we continue a series of investigations about seismic DA procedures and expand on them by analyzing ways to assimilate more than one seismic monitor. More specifically, we evaluate different ways of using production data and two monitor surveys (M3 and M5) to calibrate the dynamic models of a real Brazilian reservoir using the ensemble smoother with multiple data assimilation (ES-MDA) method. We performed the following experiments: (1) sequential assimilation of M3 and M5 with parts of well history divided according to the seismic acquisition dates; (2) assimilation of M3 with the entire well history and subsequent assimilation of M5; (3) assimilation of well and M3 data; and (4) assimilation of well and M5 data. For comparison purposes, we also assimilated only well data. From the results, we observed that well and 4DS data misfits were reduced when assimilating both monitors, compared to the cases where only a single monitor (any of them) was used with production data. This conclusion is also true in the comparison with results obtained when only assimilating well data. This indicates that both seismic monitors are important data to be quantitatively considered in DA. In this particular field, using a previous DA run to solely assimilate the newly available monitor (Case 2) delivered better models and long-term forecasts. Therefore, this would be our recommendation. This study highlights the importance of several 4DS acquisitions for reservoir monitoring and management and shows the challenges of their application in seismic DA for better life cycle field applications.

Introduction to this special section: Seismic reservoir modeling

The seismic data set is a fundamental requirement for producing oil and gas fields. It provides understanding of the structure and stratigraphy of the reservoirs, and it is now routinely employed in reservoir modeling for advancing insights into how fields are being produced. Modern 3D seismic data were first developed in the 1960s, but it wasn't until the 1980s that seismic interpretation software enabled the building of gridded reservoir models from seismic interpretations. Reservoir modeling utilizing seismic interpretations drove insights into reservoir quality and performance, helping to understand the communication between reservoir units and wells, particularly in fields with many wells. But key challenges such as the cost of building or updating reservoir models and scale variance created barriers for early industry-wide adoption. Data integration required calibration to correct and account for the difference in measurement scales of seismic data and well data, as well as to create robust relationships between seismic properties and petrophysical properties in the model. Over time, technological advancements led to a reduction in the cost of reservoir modeling, while increased acquisition, processing, and utility of seismic data provided the means to drive innovation toward incorporating seismic. Today, 3D and 4D seismic data play pivotal roles in defining and updating reservoir models where hundreds to thousands of simulations can be realized in a reservoir model to explore history matching and model uncertainties.

Reliable channel reservoir characterization and uncertainty quantification using variational autoencoder and ensemble smoother with multiple data assimilation

Reservoir characterization is essential for reliable performance prediction and decision making. In this study, a reliable scheme is suggested for channel reservoir characterization and uncertainty quantification using variational autoencoder(VAE) and ensemble smoother with multiple data assimilation(ES-MDA). The scheme composes of three stages. First, rock facies of channel reservoir models are used to train a VAE network. Second, the latent vectors in VAE are updated via ES-MDA by considering observation data. Finally, updated latent vectors are decoded to restore rock facies of the channel reservoir models. The proposed scheme shows superior capability of model calibration compared to ES-MDA algorithm for all three channel reservoirs cases analyzed. It successfully detects channel patterns of reference models and also prevents permeability from exceeding unreal value, which is a major problem of ES-MDA. On the top of that, more reliable future production forecast is achieved from the models updated by the proposed method. • This paper suggests a new scheme for channel reservoir characterization by VAE and ES-MDA. • ES-MDA updates the latent vectors in VAE aiming to coincide with observed data. • Updated latent vectors are decoded into rock facies to get updated reservoir models. • The scheme gives reliable update of channel reservoir model and future performance prediction.

Stepwise uncertainty reduction in time-lapse seismic interpretation using multi-attribute analysis

Recently, time-lapse seismic (4D seismic) has been steadily used to demonstrate the relation between field depletion and 4D seismic response, and, subsequently, to provide more efficient field management. A key component of reservoir monitoring is the knowledge of fluid movement and pressure variation. This information is vital in assisting infill drilling and as a reliable source of data to update reservoir models, and, consequently, in helping to improve model-based reservoir management and decision-making processes. However, in practice, varying levels of uncertainty are inherent in the 4D seismic interpretation of reservoirs that uses a multipart production regime. The complex nature of some 4D seismic signals emphasizes the role of the competing effects of geology, rock and fluid interactions. Hence, a reliable 4D interpretation requires an interdisciplinary approach that entails data analysis and insights from geophysics, engineering and geology. In this study, a stepwise workflow was introduced to reduce the uncertainties in the 4D seismic interpretation and to identify the improvements required in order to perform better reservoir surveillance. In parallel, the workflow demonstrates the use of engineering data analysis in conducting a consistent interpretation, and encompasses the 3D and 4D seismic attributes with engineering data analysis. This study was carried out in a Brazilian heavy-oil offshore field where production started in 2013. The field experienced intense production activity up to 2016, making the deep-water field an ideal candidate to explore the challenges in interpreting complex 4D signals. Beyond these challenges, a significant understanding of reservoir behaviour is obtained and improvements to the reservoir simulation model are suggested that could assist reservoir engineers with data assimilation applications.

Bayesian full waveform inversion in anisotropic elastic media using the iterated extended Kalman filter

Uncertainty quantification in the context of seismic imaging is important for interpretinginverted subsurface models and updating reservoir models. The limited illumination, noisydata and poor initial model in the seismic full waveform inversion (FWI) lead to inversionuncertainties. This is particularly true for anisotropic elastic FWI, which suffers from extra parameter trade-off problems. In this work, we address the uncertainty quantificationof anisotropic elastic FWI problem in the framework of Bayesian inference. Specially, weestimate the uncertainties of the subsurface elastic parameters in the Bayesian anisotropicelastic FWI by combining the iterated extended Kalman filter with an explicit representation of the sensitivity matrix with Green’s functions. The sensitivity matrix is based onthe integral equation approach, which is also within the context of nonlinear inverse scattering theory. We give the results of numerical tests with examples for anisotropic elasticmedia. They show that the proposed Bayesian inversion method can provide reasonablereconstructed results for the elastic coefficients of the stiffness tensor and the framework issuitable for accessing the uncertainties.

Iterated extended Kalman filter method for time‐lapse seismic full‐waveform inversion

ABSTRACTTime‐lapse seismic data is useful for identifying fluid movement and pressure and saturation changes in a petroleum reservoir and for monitoring of CO2 injection. The focus of this paper is estimation of time‐lapse changes with uncertainty quantification using full‐waveform inversion. The purpose of also estimating the uncertainty in the inverted parameters is to be able to use the inverted seismic data quantitatively for updating reservoir models with ensemble‐based methods. We perform Bayesian inversion of seismic waveform data in the frequency domain by combining an iterated extended Kalman filter with an explicit representation of the sensitivity matrix in terms of Green functions (acoustic approximation). Using this method, we test different strategies for inversion of the time‐lapse seismic data with uncertainty. We compare the results from a sequential strategy (making a prior from the monitor survey using the inverted baseline survey) with a double difference strategy (inverting the difference between the monitor and baseline data). We apply the methods to a subset of the Marmousi2 P‐velocity model. Both strategies performed well and relatively good estimates of the monitor velocities and the time‐lapse differences were obtained. For the estimated time‐lapse differences, the double difference strategy gave the lowest errors.

Correlation-Based Adaptive Localization With Applications to Ensemble-Based 4D-Seismic History Matching

Summary Ensemble-based history-matching methods have received much attention in reservoir engineering. In real applications, small ensembles are often used in reservoir simulations to reduce the computational costs. A small ensemble size may lead to ensemble collapse, a phenomenon in which the spread of the ensemble of history-matched reservoir models becomes artificially small. Ensemble collapse is not desired for an ensemble-based history-matching method because it not only deteriorates the capacity in uncertainty quantification, but also forces the ensemble-based method to later stop updating reservoir models. In practice, distance-based localization is thus introduced to tackle ensemble collapse. Distance-based localization works well in many problems. However, one prerequisite in using distance-based localization is that the observations have associated physical locations. In certain circumstances with complex observations, this may not be true, and it thus becomes challenging to apply distance-based localization. In this work, we propose a correlation-based adaptive localization scheme that does not rely on the physical locations of the observations. Instead, we use the spatial distributions of the correlations between model variables and the corresponding simulated observations. In the course of history matching, we update model variables by only using the observations that have relatively high correlations with them, while excluding those that have relatively low correlations. This is equivalent to introducing a data-selection procedure to the history-matching algorithm. As a result, the threshold values for data selection play an essential role in the proposed adaptive localization scheme, and we develop both ideal and practical approaches to the choices of the threshold values. We demonstrate the efficacy of the proposed localization scheme using seismic history-matching problems—one 2D and one 3D—in which ensemble collapse is severe in the presence of large amounts of observational data, but distance-based localization may not be applicable because of the lack of physical locations of the seismic data in use. In contrast, correlation-based localization works well to prevent ensemble collapse and also renders good history-matching results. We also note some practical conveniences of the proposed localization scheme, including the applicability to nonlocal observations, the relative simplicity in implementation, the transferability of the same codes among different (either 2D or 3D) case studies, and the adaptivity to different types of observations and petrophysical parameters.

Using simulation and production data to resolve ambiguity in interpreting 4D seismic inverted impedance in the Norne Field

The Norne Field started production in 1997 and up to 2006 the field experienced intense production activity, making the Norne benchmark case an ideal candidate to explore the challenges in interpreting complex time-lapse seismic data. Seismic amplitude changes and time-shifts are used as the first level approach to interpret the time-lapse differences and to update reservoir models. A common alternative is to invert the seismic data and obtain acoustic impedance variations caused by production activity, and to evaluate their possible interpretations. For this case study, we use a 4D inversion approach to invert the base (2001) and monitor (2006) seismic surveys in order to provide field-wide insights for the Norne benchmark case. We extensively interpret the observed 4D inversion anomalies and decouple, as much as possible, the effects of fluid and pressure variations, supported by production and reservoir engineering data. Moreover, we compare the inversion results with the simulation model from the Norne benchmark case to suggest areas of future modification to the simulation model. This research is intended as a resource to improve the quality of history matching or other 4D inversion methods applied to the Norne benchmark case, and to demonstrate a detailed time-lapse seismic interpretation within the reservoir segments of the Norne Field. Supplementary material: Well-history data of six producer and injector wells is available at https://doi.org/10.6084/m9.figshare.c.3890251

Simultaneous Estimation of Relative Permeability and Capillary Pressure for PUNQ-S3 Model With a Damped Iterative-Ensemble-Kalman-Filter Technique

Summary A damped iterative-ensemble-Kalman-filter (IEnKF) algorithm has been proposed to estimate relative permeability and capillary pressure curves simultaneously for the PUNQ-S3 model, while its performance has been compared with that of the CMOST module, iterative-ensemble-smoother (IES) algorithm, and traditional ensemble-Kalman-filter (EnKF) technique. The power-law model is used to represent the relative permeability and capillary pressure curves, while three-phase relative permeability for oil phase is determined by use of the modified Stone II model. By assimilating the observed production data, the relative permeability and capillary pressure curves are inversely, automatically, and successively updated, achieving an excellent agreement with the reference cases. Not only are the associated uncertainties reduced significantly during the updating process, but also each of the updated reservoir models predicts the production profile that is in good agreement with the reference cases. Although the damped IEnKF technique shows the highest accuracy on estimation results, history-matching results, and prediction performance for the PUNQ-S3 model, its computational expense is still high compared with the other three techniques. In addition, the variations in the ensemble of the updated reservoir models and production profiles of the damped IEnKF provide a robust and consistent framework for uncertainty analysis.

Characterization of channelized gas reservoirs using ensemble Kalman filter with application of discrete cosine transformation

Reservoir characterization is a process to make a model similar to a true reservoir by integrating available data. It is necessary for reliable estimation of future productions and making proper decisions. Ensemble Kalman filter is one of the most powerful reservoir characterization methods. Ensemble Kalman filter can update reservoir models in real time and assess uncertainty by multiple models. Characterization of a channelized reservoir is difficult because of irregular channel pattern and connectivity. In this paper, ensemble Kalman filter with preservation of facies ratio and discrete cosine transformation is proposed for solving overshooting problem and maintaining properties of channel. The proposed method gives us stable characterization performances on gas and water productions, channel pattern and connectivity, and aquifer strengths regardless of the existence of an aquifer and uncertainty of facies ratio. Although facies ratio has ± 10% uncertainty with the reference, updated models maintain channel properties properly. Discrete cosine transformation helps to preserve channel pattern and connectivity and, therefore, overall performances are enhanced. Consequently, the proposed method can provide reliable updated models and credible prediction of future reservoir performances for making a proper decision.

Updating joint uncertainty in trend and depositional scenario for reservoir exploration and early appraisal

Computationally efficient updating of reservoir models with new production data has received considerable attention recently. In this paper however, we focus on the challenges of updating reservoir models prior to production, in particular when new exploration wells are drilled. At this stage, uncertainty in the depositional model is highly impactful in terms of risk and decision making. Mathematically, such uncertainty is often decomposed into uncertainty of lithological trends in facies proportions which is typically informed by seismic data, and sub-seismic variability often modeled geostatistically by means of training images. While uncertainty in the training image has received considerable attention, uncertainty in the trend/facies proportion receives little to no consideration. In many practical applications, with either poor geophysical data or little well information, the trend is often as uncertain as the training image, yet is often fixed, leading to unrealistic uncertainty models. The problem is addressed through a hierarchical model of probability. Total model uncertainty is divided into first uncertainty in the training image, then uncertainty in the trend given the uncertain training image. Our methodology relies on an efficient Bayesian updating of these model parameters (trend and training image) by modeling forward-simulated well facies profiles in low-dimensional metric space. We apply this methodology to a real field case study involving wells drilled sequentially in the subsurface, where as more data becomes available, uncertainty in both training image and trend require updating to improve characterization of the facies.

Real-time reservoir model updating in thermal recovery: Application of analytical proxies and Kalman filtering

Abstract Kalman filtering has many applications in engineering and technology development and has been widely employed in applied geosciences. As an estimator, it is used to predict system behavior for forecasting analyses. It considers observations obtained over time with uncertainties and other inaccuracies to simulate system behavior. This study customizes a physics based analytical proxy with the extended Kalman filter for nonlinear systems to characterize reservoir rock properties including porosity and permeability under the thermal recovery process. The analytical proxy represents a reservoir simulator for thermally enhanced oil recovery, and accounts for flow and geomechanical processes for unconventional heavy oil reservoirs. This paper also shows how inverse theories can be utilized to rank and update reservoir models.

Laggan Field 3D geological modelling: case study

Abstract Drilling of Laggan development wells commenced in summer 2013 and finalization of well locations required updated reservoir models, fully incorporating the 2009 baseline seismic. Consequently, geomodelling workflows have been developed based on the 3D seismic, petrophysical and production test data. The reservoir model building methodology for Laggan consists of the following steps: (1) construction of a classical structural framework using seismic interpreted fault sticks, top reservoir depth surface and the overall reservoir thickness map estimated from seismic data; (2) building of zones model integrating well data, seismic and sedimentological concepts; and (3) generation of a 3D facies and petrophysical model constrained by seismic and well data, including well test results to calibrate the permeability. A reasonable match of the well test data was achieved without any major adjustments to the reservoir model. This methodology has led to the construction of a robust reservoir description, capturing the geological knowledge of the turbidite system and constrained by well and seismic data. The new Laggan model is now considered ‘fit for purpose’ for well locations update, well type and well number confirmation and development of the Reservoir Management Plan.

Rapid Updating of Stochastic Models by Use of an Ensemble-Filter Approach

Summary Applying an ensemble Kalman filter (EnKF) is an effective method for reservoir history matching. The underlying principle is that an initial ensemble of stochastic models can be progressively updated to reflect measured values as they become available. The EnKF performance is only optimal, however, if the prior-state vector is linearly related to the predicted data and if the joint distribution of the prior-state vector is multivariate Gaussian. Therefore, it is challenging to implement the filtering scheme for non-Gaussian random fields, such as channelized reservoirs, in which the continuity of permeability extremes is well-preserved. In this paper, we develop a methodology by combining model classification with multidimensional scaling (MDS) and the EnKF to create rapidly updating models of a channelized reservoir. A dissimilarity matrix is computed by use of the dynamic responses of ensemble members. This dissimilarity matrix is transformed into a lower-dimensional space by use of MDS. Responses mapped in the lower-dimension space are clustered, and on the basis of the distances between the models in a cluster and the actual observed response, the closest models to the observed response are retrieved. Model updates within the closest cluster are performed with EnKF equations. The results of an update are used to resample new models for the next step. Two-dimensional, waterflooding examples of channelized reservoirs are provided to demonstrate the applicability of the proposed method. The obtained results demonstrate that the proposed algorithm is viable both for sequentially updating reservoir models and for preserving channel features after the data-assimilation process.

Snøhvit: The History of Injecting and Storing 1 Mt CO2 in the Fluvial Tubåen Fm

The Snøhvit CO2 injection into the Tubåen Formation ended in April 2011. This paper summarizes the Statoil experiences from the injection regarding operational aspects, monitoring and simulation of the CO2 flow in the reservoir. The use of down-hole pressure measurement, in combination with repeated surface seismic data, improved the understanding of the injection process. Detailed interpretation of fall-off pressures in combination with good and updated reservoir models and thorough investigations into the rock mechanical strength of the reservoir rock lead eventually to the abandonment of the original injection reservoir. The storage capacity of the Tubåen Formation is not reached, but the well and the near well reservoir could not receive the necessary volume rate. A PLT -log was run during abandonment confirming pressures and flow scenario and thereby the previous interpretations. The CO2 injection at Snøhvit continues at normal levels in a fallback reservoir. No CO2 has been or will be vented to the atmosphere.

Pressure-Transient and Production-Data Analysis of an Unconsolidated Formation

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 147506, ’Pressure-Transient and Production-Data Analysis of a Horizontal Well in an Unconsolidated Formation in Frade, Brazil,’ by Yan Pan, SPE, Russ Ewy, SPE, Don Ringe, SPE, Medhat M. Kamal, SPE, Ralph Affinito, SPE, and Oluwole Sotunde, SPE, Chevron, prepared for the 2011 SPE Annual Technical Conference and Exhibition, Denver, 30 October-2 November. The paper has not been peer reviewed. Frade Asset is a medium-heavy-oil field in the northern Campos basin, approximately 75 miles offshore Rio de Janeiro in 3,500-ft water depth. The structure is a low-relief anticline with two main fault blocks consisting of four stacked unconsolidated reservoirs. The field produces from nine horizontal oil wells with three vertical water-injection wells to maintain reservoir pressure. Continuous well monitoring and reservoir characterization are key to cost-efficient development in this deepwater subsea field. Introduction The development strategy for the field includes horizontal production wells and deviated injection wells to maintain reservoir pressure. The asset-management team realized the challenges of this particular field and made the decision to install permanent downhole gauges in every production and injection well drilled, with a reservoir-surveillance plan in place to monitor well and reservoir performance. Active reservoir surveillance enabled engineers to make sound and quick operating decisions, such as shutting in wells, changing chokes, or planning well interventions on the basis of dynamic data collected continuously at wells. It also provided real-time information for improved reservoir characterization, model forecasts, and optimized development plans. Because data stream in continuously, the cycle from data collection to updating reservoir models to forecasting field performance to optimizing development could be shortened. Example Well: Producer 2 Well Producer 2 is in the upthrown fault block, as shown in Fig. 1, and had an original reservoir pressure of 3,079 psia and a reservoir temperature of 110°F. The net pay thickness is approximately 42 m, and the horizontal well was drilled 30 m above the sealing bottom of the reservoir. The well trajectory was parallel to the fault to the southwest and to the axial fairway edge to the northeast. The well was completed with an openhole gravel pack, and the total completion length was 507 m. The permanent downhole gauge was installed above the top of the gravel-pack completion.

Gravimetric Monitoring of Water Influx Into a Gas Reservoir: A Numerical Study Based on the Ensemble Kalman Filter

Summary Water influx into gas fields can reduce recovery factors by 10–40%. Therefore, information about the magnitude and spatial distribution of water influx is essential for efficient management of waterdrive gas reservoirs. Modern geophysical techniques such as gravimetry may provide a direct measure of mass redistribution below the surface, yielding additional and valuable information for reservoir monitoring. In this paper, we investigate the added value of gravimetric observations for water-influx monitoring into a gas field. For this purpose, we use data assimilation with the ensemble Kalman filter (EnKF) method. To understand better the limitations of the gravimetric technique, a sensitivity study is performed. For a simplified gas-reservoir model, we assimilate the synthetic gravity measurements and estimate reservoir permeability. The updated reservoir model is used to predict the water-front position. We consider a number of possible scenarios, making various assumptions on the level of gravity measurement noise and on the distance from the gravity observation network to the reservoir formation. The results show that with increasing gravimetric noise and/or distance, the updated model permeability becomes smoother and its variance higher. Finally, we investigate the effect of a combined assimilation of gravity and production data. In the case when only production observations are used, the permeability estimates far from the wells can be erroneous, despite a very accurate history match of the data. In the case when both production and gravity data are combined within a single data assimilation framework, we obtain a considerably improved estimation of the reservoir permeability and an improved understanding of the subsurface mass flow. These results illustrate the complementarity of both types of measurements, and more generally, the experiments show clearly the added value of gravity data for monitoring water influx into a gas field.

Crossing the Technology Chasm: Permanent Downhole Monitoring

Editor’s note: This is the third and final installment of a multipart series examining key upstream technology uptake challenges in the oil and gas industry, and the reasons for the lack of accelerated acceptance of viable technologies needed to exploit increasingly challenging drilling and production environments. Similar to the managed pressure drilling (MPD) article published in the February JPT and the seismic while drilling (SWD) article in March, a permanent downhole monitoring (PDM) survey was conducted among SPE readership around the world. In total, about 1,500 SPE members responded to the survey. The majority of the respondents were operators (64%) and technology providers (13%). Unlike the previous two technologies analyzed, PDM generally has a higher level of acceptance among operators because it has been on the market for a longer time, and because significant reliability and performance improvements have occurred over the past several years. A key question in the survey was: “What are the main value propositions you see for permanent downhole monitoring?” As shown in Fig. 1, the most important value propositions were: 1) optimizing production through continuous information, 2) avoiding the necessity to shut in the well to obtain pressure ratings, and 3) emerging distributed temperature capabilities that add further value in understanding and optimizing production. There was general agreement among both operators and technology providers on the importance of these benefits. The relevance of these value propositions was stated by Shahab Mohaghegh, president of Intelligent Solutions: “The thought of being able to know what is going on in the well and in the reservoir in real time is very exciting. You imagine sitting in your office thousands of miles away from the field and being able to decide what needs to be done and then being able to see the consequences of your decision in near real time.” A similar view was expressed by Garrett Skaggs, monitoring product champion at Schlumberger. “The value of permanent downhole monitoring systems stems from the ability to provide continuous, reliable well and reservoir performance information without the need for intervention,” he said. “These measurements enable operators to manage decisions regarding hydrocarbon assets including production optimization, problem identification and diagnosis, updating reservoir models, and field development planning.” The growing importance of fiber optic technology and distributed temperature sensing was also raised by a number of operators. Significant improvements in these systems over the past few years have led to an increased acceptance of this downhole technology. In the view of Glynn McColpin, director of reservoir monitoring at Pinnacle (a Halliburton service): “I think we are actually seeing the next wave with fiber optic sensing. Electronics can only get you to a certain temperature, then you start worrying about longevity and failures. You start looking at the fiber optic solutions coming out—we can go into steam wells up to 350°C with just glass and the glass itself is a sensor.”