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Abstract Reservoir models were constructed on two gas condensate reservoirs (M-4/M4-A and M-6) in the Gulf of Mexico. The gas reservoirs are at a depth of 15000 feet subsea, over pressured and supported by active aquifers. Initial gas production was essentially water-free with steep pressure decline that was later followed by rapid rise in water production. Peak gas production from either reservoir reached about 166 MMCFPD. The reservoir models were fully compositional. The models were history-matched with production data and then used to predict future performance. This paper compares predictions of the models to actual reservoir production data. The model for the M-6 reservoir predicted gas production with an accuracy of -0.72%, condensate production at +2.80% and water production at +12.4%. The predicted accuracies for the M-4/4A reservoirs are as follows: gas (-0.33%), condensate (-4.99%) and water (+25.56%). The main objective of this paper is to provide data on the errors associated with the use of history matched models to forecast reservoir performance. Surveys of the petroleum literature indicate that they are few technical papers that compare model predictions to actual reservoir performance data over an extended period of time. Mass publications of these types of results are essential in establishing reservoir simulation as a valuable analytical tool for reservoir engineers. It will also improve its acceptance in the petroleum industry as a reliable tool for prediction of reservoir performance. Introduction The M-4/M-4A and M-6 sands are mid-size, over pressured and moderately rich gas condensate reservoirs located in the Gulf of Mexico at a water depth of about 240 feet. At their peak, gas production rate from each reservoir averaged 166 MMCFPD. Production data show that both reservoirs were supported by very strong aquifers. Two fully compositional models were constructed for the M-4/M-4A and M-6 reservoirs. The models were executed on VIP-COMP simulator from Landmark Graphics Corporation. This work is a direct application of reservoir simulation technology for prediction of reservoir performance especially in terms of fluid production rates and cumulative recoveries. The techniques or work processes used in the study are basic and routine. The relevance of the work lies in comparing model predictions to actual production data from the reservoirs. Generally, few technical papers have been published that compare model predictions to actual reservoir performance data over an extended period of time. As reservoir simulation develops into a tool used by most reservoir engineers, experienced practitioners of the art of reservoir modeling must strive to provide to the technical literature data that quantify errors associated with predictions from reservoir models. Concerted efforts in this area is needed at this stage of evolution of reservoir modeling It will accelerate acceptance of reservoir modeling as a reliable tool for prediction of reservoir performance.
Fiber-optic sensing technology is the right technology for Permanent Reservoir Monitoring (PRM) applications due to significant advantages in reliability, system design and installation flexibility, and enhanced compatibility with future field development trends.
It is well known that fiber-optic technology is inherently a more reliable technology choice for underwater applications. This has been proven by long-term use within military applications and the telecommunications industry. The application of fiber-optic technology for PRM offers the proven reliability benefits long recognized by the telecommunications industry to the oil and gas industry. The use of fiber-optic sensing technology removes the electronics and electrical power requirements from the seabed and replaces it with completely electrically passive components, which can be interrogated from a platform, FPSO or from shore, using only optical signals. This eliminates the potential for electrical leakage and decreases the potential for mechanical water leak-induced failures in the underwater equipment. The improved reliability drives down the through-life operations and maintenance costs for PRM applications.
It is less well known that the inherent flexibility available with fiber-optic PRM systems generates significant additional value beyond the reliability advantage. Fiber-optic systems enable more flexible system design and layout solutions compared to electrical based alternatives. Greater design and layout flexibility translates into significant advantages during system installation, which can reduce the cost and risk of PRM projects. Advanced optical architectures and proprietary system design techniques generate an expanded range of layout and connection options, which can be used to optimize the system supply and installation solutions during an integrated PRM project planning process.
The authors conclude that PRM systems based on fiber-optic technology offer a better platform for the future due to a number of key advantages. Fiber-optic systems offer greater potential for cost-effective expansion, technology upgrade and cost reduction moving forward. The very low propagation loss intrinsic to fiber-optic technology makes it ideally suited to address a number of developing trends in offshore field development.
In this paper, the authors present examples of clear advantages in reliability, flexibility, expandability and suitability to address future trends for operators to consider when selecting a PRM system for 4D seismic monitoring. Based on these examples and evidence, the authors conclude that fiber-optic sensing is the technology of choice for PRM.
This paper presents the results from two simulation parametric studies of coalbed methane well forecasting techniques. Phase I qualitatively identifies the relative impact of key reservoir properties, and rank orders them. Phase II uses a Monte Carlo simulation approach to quantify the degree of confidence associated with coalbed methane well forecasts for two sets of distributions of key reservoir properties. Results indicate that the range of error associated with the best currently-available reservoir property measurement techniques significantly impedes the engineer's ability to accurately forecast production with confidence, and points to the need for improved measurement accuracy for key reservoir properties.
Similar to conventional oil and gas reservoirs, it is important for reservoir engineers to generate production forecasts for coalbed methane wells to evaluate the value of the in-place reserves, design field facilities, and optimize development practices. The process of forecasting coalbed methane well production, however, has proven to be relatively difficult compared the process of forecasting production for conventional gas wells due to the relatively complex nature of the methane storage and flow mechanisms in coals.
The two techniques that have been used for predicting coalbed methane well production are (1) decline curves, and (2) reservoir simulation. Several recent papers have illustrated the use of decline curve techniques as applied to producing coalbed methane wells. Although there is little theoretical or historical basis for applying decline curves to coalbed methane wells, these authors have shown that, once producing coalbed methane wells exhibit a sustained declining production trend, decline curves can adequately match the production decline history. Presumably, decline curves can be used to predict future well production for these wells. The major drawback of using decline curves for forecasting coalbed methane well production is that a relatively long producing history (more than 2 years) is required to establish the appropriate decline rate. It has also been shown that the decline rate of wells within a field varies significantly. Therefore, it is extremely difficult to use decline curves to accurately forecast production for offset acreage, infill well locations, or producing wells that have not yet exhibited a declining production trend.
Reservoir simulators are the best available tool for predicting long-term production of coalbed methane wells. Coalbed methane reservoir simulators are the only tool available to reservoir engineers that can correctly account for gas desorption, methane diffusion characteristics, relative permeability effects, well to well interference effects, and well operating procedures simultaneously. Simulators are also able to account for the dynamics of permeability and porosity variations over time that may occur as a result of the large, inherent compressibility of coal.
The main disadvantage of using reservoir simulators to forecast coalbed methane well production is that a relatively large amount of data must be assembled to run the simulator. Table 1 is a listing of the properties required for simulating coalbed methane well production, and the common sources of these data. Because the data used to construct a simulation dataset are obtained from a variety of sources, and because many of the measurements are from core tests (which may need to be modified for use as input into a simulator due to scaling effects), there is obvious concern over the accuracy of a production forecast generated using these data.
Summary Decline curves are the simplest type of model to use to forecast production from oil and gas reservoirs. Using a selected decline model and observed production data, a trend is projected to predict future well performance and reserves. Despite capturing general trends, these models are not sufficient at describing the underlying physics of complex multiphase porous-media flow phenomena and at explaining variations in production caused by changes in operational conditions. The application of these models within a Bayesian framework is a feasible alternative to mitigate this issue and obtain more‐robust forecasts by representing the possible outcomes with probability distributions. However, one important aspect that conditions the production forecasts and their uncertainty is the design of a suitable prior distribution, which can be subjective. To address the aforementioned issue, this paper presents a workflow for the development of a localized prior distribution for new wells drilled in shale formations that combines production data from pre‐existing surrounding wells and spatial data, specifically well‐surface/bottom coordinates. This workflow aims to establish engineering criteria to reduce the subjectivity in the design of a prior distribution, assessing spatial continuity of the parameters of a physics‐based decline‐curve model (θ2 model), automatically identifying regions where uncertainty can be reduced a priori, and reliably quantifying the uncertainty. A case study of 814 gas wells in the Barnett Shale is presented, and several maps are generated for the analysis of important properties to be considered during field development. The dry‐gas window presented more‐continuous decline‐curve parameters than the wet‐gas and gas/condensate windows, which resulted in lower uncertainty with the localized prior approach. As more data are acquired with time, the uncertainty in the production forecasts is further reduced and the localized prior becomes more informative, especially in the dry‐gas window. The localized prior can then serve as an indicator for the performance of new infill wells in different locations. Portions of the content of this paper were initially presented in Holanda et al. (2018b), and are further developed and reviewed here.
Abstract Due to suppressed natural gas price in the past several years in North America, liquid-rich retrograde gas reservoir development has been the main focus for many gas reservoir operators in Canada. Due to the subsurface complexity of PVT behavior for condensate, liquid (condensate) production forecast has been a challenge for operators. In addition, many liquid-rich retrograde reservoirs have also encountered extremely low permeability, which makes the liquid production forecast an even more challenging task for operators. Today, the most common methodologies to analyze production performance for retrograde gas reservoirs are limited to either numerical (simulation) or empirical (such as Arps’ decline). However, for numerical analysis, original PVT properties, special core analysis (SCAL) and pressure history are required as input data, which are usually very costly to obtain and they are, therefore, routinely ignored by operators. This paper presents a simple way to predict condensate production from the gas production by means of readily available early years’ production data. This simple methodology includes a new specialized plot to find related parameters for condensate production forecast without any costly PVT and pressure history data. Moreover, a set of diagnostic plots has been developed to identify the degree of the blockage to the gas production from the near wellbore oil-bank. This new methodology has been tested on more than one hundred horizontal wells that have been producing retrograde gas from several Western Canadian formations, such as the Notikewin, Glauconite, Montney, Falher as well as the Eagle Ford formation in the United States. All such tests were carried out by using only the early part of the production data to history-match the later part of the production history. The results have shown good agreement with the forecast based on the new methodology. Both synthetic and real well examples will be presented in this paper to illustrate the use of this new methodology.