In theory, both build-up and draw-down pressure transients can be analyzed in gas well tests. Quite frequently, only build-up pressure data is usable as the draw-down data can be quite noisy. Draw-down pressure data is often strongly affected by flow rate changes, which can only be roughly estimated in the absence of down-hole flow rate recording. Heavy data filtering can sometimes help, but such filtering can mask important reservoir features such as boundaries and double porosity. Modern well test gauges can measure temperature as accurately as pressure. Use of these temperature measurements is typically limited to correction or calibration of the raw pressure signal. Less commonly, temperature readings are used to explain anomalous pressure trends or to estimate static and flowing bottom hole temperatures. In addition to reviewing the more classical uses of temperature in wells, this work provides a new use of transient temperature data to yield valuable information in gas well tests. It presents a novel approach for estimating the down-hole flow rate from flowing temperature measurements. This is possible because the temperature departure is intimately linked to pressure, fluid composition, and fluid velocity changes. Since down hole pressure is continuously measured and fluid composition can be quantified, an estimated down-hole flow rate can be generated. This flow rate can be used to deconvolve the draw down pressure data, and the results can be compared to pressure build-up analysis results.

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Coalbed methane (CBM) reservoirs are a growing source of relatively clean energy in many parts of the world. CBM reservoirs are fundamentally different from traditional hydrocarbon reservoirs. Gas is adsorbed on the surface of the coal cleats and not stored in pores. Still, estimation of the permeability of the coal cleats is important in judging the potential producibility of a given coal seam.Traditionally, CBM reservoirs are surface tested using injection or production techniques to access reservoir permeability (Clarkson and Bustin 2011). Recently, pressure buildup and falloff tests using the straddle packer module of a wireline formation tester have been used in CBM reservoirs to assess reservoir permeability successfully. However, low permeability, limited station time, or both have, in some cases, reduced the quality of the interpretation results.Deconvolution techniques have been available for some time; however, few practical examples are available in the literature. The use of deconvolution will generally allow extracting more of the same data. The derivative response uncertainty is normally due to errors in estimating the reservoir pressure and the flow rate. Generally, wireline formation testers provide reliable measurements of the reservoir pressure and flow rate.We applied deconvolution to pressure buildup and falloff for the first time on data acquired in a CBM environment with a straddle packer. The use of deconvolution has improved the permeability estimation from the different tests. We were also able to identify the limitations of the technique and the uncertainties in the analysis results.

Over the past few years there has been a surge of interest in coal bed methane (CBM) resources in many parts of the world. Also known as coal seam gas (CSG), CBM has become an important source of energy because of increasing global demand for cleaner fuels. CBM is distinct from conventional hydrocarbon reservoirs as methane is stored within coals by adsorption. With matrix porosity generally lower than 4%, cleats and fractures are the main conduits for production from coals. Given differences in structure compared to conventional reservoirs, drilling into coal seams requires the use of minimum overbalance and nondamaging fluids. In addition, evaluation of CBM reservoirs has many technical challenges. One of the main challenges is to ascertain coal cleat behavior and estimate permeability and ultimately productivity of a target zone. Traditionally this has been done by production or injection tests using conventional testing techniques. In Australia, a wireline-deployed straddle packer configuration was used to address this challenge, with demonstrated benefits for determining permeability and productivity.Unlike traditional methods of conducting a closed chamber test across a large interval, this methodology uses a straddle packer with a downhole pump in a toolstring deployed on wireline. The packer spacing can be adjusted prior to deployment to suit the expected height of the coal bed to be tested. The tool is capable of both injection into and production from a coal bed interval with a much smaller storage volume compared to conventional test strings. Pressure is continuously monitored in real time ensuring that acceptable limits are not exceeded during either the injection or drawdown phases, to avoid excessive force on a coal seam while maintaining single-phase flow. The analysis of both drawdown-buildup and injection-falloff results reveals the strengths and limitations of the two techniques.

Two innovative approaches were used to filter pressure data. The analysis started by reviewing field geology. Core data were used to obtain average values for horizontal and vertical permeabilities. Production logging data were analyzed to get the effective well length for Well (1) and Well (2). Well test analysis proceeded at this stage. A numerical model was also constructed to confirm the results of the analysis. Some aspects of representing horizontal well in numerical reservoir simulator were discussed. Data Filtering The total number of the data points during build up period for well (1), and well (2) are in the range of 15000, and 19000 points respectively, equally spaced in time. Typical well test interpretation packages normally use a limited number of points per flow period to conduct the analysis. The way these points are chosen is normally very basic. For example, one point is chosen every nth number of data points, or a certain number of points is chosen per log cycle. To illustrate the drawback of this method, an example is given here. A point was chosen every 50 points and a log-log plot of this data is shown in Figure 1. It is obvious that any useful analysis is impossible on this set of data at its current state. Normally well interpretation software will allow the user to apply some sort of smoothing to the pressure data. This is a subjective procedure dependent on the user. There is always a risk of over smoothing and smearing the pressure data trend or under smoothing and masking the pressure data trend. Butterworth Digital Filter Method Using digital filtering technique can provide us with robust and objective method of getting rid of noise. If we consider a signal p(t) contaminated by a noise signal n(t). Then the total measured signal m(t) is given by: (1) If we plot the spectral power density of the measured signal m(t) we cannotice that the power spectrum of the signal is sticking up on a continuous noise spectrum, (Ref. 1). Ideally we want to apply an ideal low pass filter to the data to get rid of most of the noise signal. Unfortunately the implementation of an ideal filter is impossible, but there are practical approximations to it (Ref. 2). One of these approximations is the Butterworth filter. A typical response of a Butterworth filter is shown in Figure 2. This methodology is applied in our case. A plot of the power spectral density of the pressure of well (1) is shown in Figure 3. A digital Butterworth filter with a frequency cutoff of 0.07 was chosen after inspecting the spectral power density plot. The digital filtering was carried out in the frequency domain, and then was transformed back to the time domain. A plot of the power spectral density of well (1) after filtering is shown in Figure 4. The first data points in the data set will be spoiled due to transient response of the filter, this is not important, since the first data points are dominated by well bore storage. The spoiled data points were then replaced by the original data points for the 1st 300 points. A plot of the pressure derivative after using this method is also shown in Figure 5. Now we can see clearly the trend of the pressure derivative. The same method was applied to the pressure data of well (2). Most Representative Pressure Point (MRPP) Method Another method for eliminating noisy points was developed. P. 375