Permanent Downhole Gauge Data Research Articles

Technology Focus: Reservoir Surveillance (September 2024)

In today’s oil and gas operations, surveillance technologies have undergone a revolution, reshaping how facilities, wells, and reservoirs are monitored. These advancements not only have increased the scale at which these technologies are deployed but also have led to an unprecedented influx of data. The sheer volume of data, however, poses a significant challenge to traditional analytical methods, overwhelming their capacity to derive actionable insights effectively. To address this challenge, the industry is rapidly advancing toward automated solutions powered by artificial-intelligence (AI) -driven analytics. These systems automate data ingestion and use machine-learning algorithms to sift through massive data sets, identifying anomalies and prioritizing actionable insights. By automating routine surveillance tasks, engineers can focus on critical actions that deliver substantial operational benefits. For example, in paper IPTC 23912, operators successfully optimized production operations by harnessing real-time field data through smart systems, effectively managing operations with complex near-critical fluids. Similarly, in paper SPE 218470, researchers proposed a novel workflow integrating virtual flowmetering and permanent downhole gauge data for pattern recognition to enhance real-time monitoring and decision-making in petroleum and geothermal industries. Nevertheless, ensuring effective surveillance of operational assets requires a strategic approach. A valuable tool in crafting such strategies is the value of information (VOI) assessment. This method systematically evaluates how acquiring specific information can influence decision-making and operational outcomes. For instance, paper SPE 215318 highlights a field operator’s systematic approach to VOI assessment, aiming to optimize daily operations and guide future development activities. In essence, while surveillance technologies have inundated operators with unprecedented data flows, advancements in automation and AI-driven analytics offer the promise of unlocking this data’s true potential. By embracing these technologies, the oil and gas industry can navigate the complexities of the modern energy landscape with greater agility, precision, and cost-effectiveness. Recommended additional reading at OnePetro: www.onepetro.org. SPE 215119 Surveillance, Analysis, and Optimization During Active Drilling Campaign by Yanfen Zhang, Chevron, et al. OTC 35413 New Opportunities in Well and Reservoir Surveillance Using Multiple Downhole Pressure Gauges in Deepwater Injector Wells by Piyush Pankaj, ExxonMobil, et al. OTC 34863 Digital Twin for Oil-Rim Management Using Early Warning System and Exception-Based Surveillance, Offshore Malaysia by M. Mahamad Amir, Petronas, et al.

Interpreting Pressure and Flow-Rate Data From Permanent Downhole Gauges by Use of Data-Mining Approaches

Summary The permanent downhole gauge (PDG) is a promising tool for reservoir testing but has yet to reach its full potential. Generally, conventional well-testing methods are most able to use small sections of constant-flow-rate data. However, data mining, a newly developed technique in computer science, is a tool that can reveal the relationship among variables from large volumes of data. The application of data-mining algorithms to synthetic and field data has been successful in extracting the reservoir model from variable-flow-rate and pressure-transient data. In fact, because of uncertainty in flow-rate measurements, this technique is one of the few ways to make use of the flow-rate data that are now available with some modern PDG tools. The application is conducted in two steps. First, the pressure and flow-rate data from the PDG are used to train a nonparametric data-mining algorithm. The reservoir model is obtained implicitly in the form of polynomials in a high-dimensional Hilbert space defined by kernel functions when the algorithm converges after being trained to the data. Next, a specific flow-rate input (for example, constant rate) is fed into the data-mining algorithm. The data-mining algorithm will make a pressure prediction subject to the input flow rate. Because the data-mining algorithm has already obtained the reservoir model, the pressure prediction is expected to be the reservoir response given the constant flow rate. Therefore, the proposed constant flow rate and the predicted pressure reveal the reservoir model underlying the variable PDG data, without needing to specify in advance which reservoir model is to be used. Three methods (Methods A, B, and C), differing by input vectors, kernel functions, and presence of breakpoints detection, are proposed in the paper. Synthetic noisy data and real field data were used to test this approach. Methods B and C were able to satisfactorily recover the wellbore/reservoir model in most considered cases. Even in extreme cases when the flow-rate data are noisy and changing frequently, and in the absence of any shut-ins, the method was still able to extract the reservoir models.

Individual well flowing rate recovery from PDG transient pressure with either assigned daily rate or total cumulative production of the well or group of wells through wavelet approach

The advent of permanent down-hole gauge (PDG) makes the real-time reservoir monitoring and management possible, where the long term transient data can be processed to distill information about the reservoir through using recently developed wavelet transform algorithm. However, the key in analyzing the PDG data, particularly transient pressure, is to get the flowing rate history right during the pressure draw downs and build ups. In practice, the most available flowing rate data include assigned daily rate, total cumulative production of the well or group of wells with a measurement error tolerance at about ±10%. While multiphase down home rate data is rather expensive to have, the industry is desperately looking for ways to get the flowing rate history right. The second big problem in analyzing long term PDG data is that there is a time shift between the flowing rate and the corresponding transient pressure. In other words, when the rate goes up, the pressure also goes up instead of going down as expected. This caused a big problem in reservoir simulation, history matching and numerical well testing. This paper presents a technique based on the recently developed wavelet approach to recover flowing rate history directly from the measured PDG transient pressure, under the most common practical conditions described above. In this method, the exact timing of the rate change is identified through wavelet high frequency analysis. Then the rate and pressure are synchronized in every identified time intervals. Since the rate and the frequency amplitude of the transformed transient pressure are proportional, the real rate value in each time interval can be derived under the given common practical conditions. Both synthetic and field examples were used to demonstrate and further validate the developed technique, which showed very promising results.

Deconvolution of Variable-Rate Reservoir Performance Data Using B-Splines

Summary We use B-splines for representing the derivative of the unknown unit-rate drawdown pressure and numerical inversion of the Laplace transform to formulate a new deconvolution algorithm. When significant errors and inconsistencies are present in the data functions, direct and indirect regularization methods are incorporated. We provide examples of under- and over-regularization, and we discuss procedures for ensuring proper regularization. We validate our method using synthetic examples generated without and with errors (up to 10%). Upon validation, we then demonstrate our deconvolution method using a variety of field cases, including traditional well tests, permanent downhole gauge data, and production data. Our work suggests that the new deconvolution method has broad applicability in variable rate/pressure problems and can be implemented in typical well-test and production-data-analysis applications. Introduction The constant-rate drawdown pressure behavior of a well/reservoir system is the primary signature used to classify/establish the characteristic reservoir model. Transient-well-test procedures typically are designed to create a pair of controlled flow periods (a pressure-drawdown/-buildup sequence) and to convert the last part of the response (the pressure buildup) to an equivalent constant-rate drawdown by means of special time transforms. However, the presence of wellbore storage, previous flow history, and rate variations may mask or distort characteristic features in the pressure and rate responses. With the ever-increasing ability to observe downhole rates, it has long been recognized that variable-rate deconvolution should be a viable option to traditional well-testing methods because deconvolution can provide an equivalent constant-rate response for the entire time span of observation. This potential advantage of variable-rate deconvolution has become particularly obvious with the appearance of permanent downhole instrumentation. First and foremost, variable-rate deconvolution is mathematically ill-conditioned; while numerous methods have been developed and applied to deconvolve "ideal" data, very few deconvolution methods perform well in practice. The ill-conditioned nature of the deconvolution problem means that small changes in the input data cause large variations in the deconvolved constant-rate pressures. Mathematically, we are attempting to solve a first-kind Volterra equation [see Lamm (2000)] that is ill-posed. However, in our case the kernel of the Volterra-type equation is the flow-rate function (i.e., the generating function); this function is not known analytically but, rather, is approximated from the observed flow rates. In practical terms, this issue adds to the complexity of the problem (Stewart et al. 1983). In the literature related to variable-rate deconvolution, we find the development of two basic concepts. One concept is to incorporate an a priori knowledge regarding the properties of the deconvolved constant-rate response. The observations of Coats et al. (1964) on the strict monotonicity of the solution led Kuchuk et al. (1990) to impose a "nonpositive second derivative" constraint on pressure response. In some respects, this tradition is maintained in the work given by von Schroeter et al. (2004), Levitan (2003), and Gringarten et al. (2003) when they incorporate non-negativity in the "encoding of the solution." We note that in the examples given, this concept (non-negativity/monotonicity of the solution) requires less-straightforward numerical methods (e.g., nonlinear least-squares minimization). The second concept is to use a certain level of regularization (von Schroeter et al. 2004; Levitan 2003; Gringarten et al. 2003), where "regularization" is defined as the act or process of making a system regular or standard (smoothing or eliminating nonstandard or irregular response features). Regularization can be performed indirectly, by representing the desired solution with a restricted number of "elements," or directly, by penalizing the nonsmoothness of the solution. In either case, the additional degree of freedom (the regularization parameter) has to be established, where this is facilitated by the discrepancy principle (effectively tuning the regularization parameter to a maximum value while not causing intolerable deviation between the model and the observations). In some fashion, each deconvolution algorithm developed to date combines these two concepts (non-negativity/monotonicity of the solution or regularization).

New Approaches for Permanent Downhole Gauge (PDG) Data Processing

One of the major issues in processing permanent downhole gauge (PDG) data is that too many transients exist over a reasonable time period, say 6 months. A formula was proposed to predict the transients that may be detected or missed. Reasonable prediction was achieved via the formula. Noise usually exists in data recorded by PDGs. Denoising is thus one of the most important steps in PDG data processing. In order to denoise the data, the data noise level must be estimated beforehand. Unfortunately, the data noise level is typically case-dependent, and therefore, it is impossible to identify a universal value for the level that may be used for all the application scenarios. One appropriate approach to estimate the noise level is to first best fit the data, subtract the predicted pressure response from recorded values, and then calculate the noise level based on the difference. We propose to apply nonlinear regression via the Polytope method (Gill, Murray, and Wright, 1981) for best-fitting PDG data to determine the noise level. It is found that the new approach is superior to the least square error (LSE) linear regression as used by Khong (2001), because the bottom-hole wellbore pressure response in a well should be treated as a nonlinear function of time over the majority of the well production/injection/shut-in period. Unless a very small range of the data (say 2 h) is considered, a linear pressure response with time is not anticipated. Furthermore, with nonlinear regression through the Polytope approach, there is no strong restriction in data quantity and data density, hence, automatic detection of the data noise level can be implemented.