One role of the petrophysicist is to characterize the fluids encountered in the reservoir. Detection of a change in fluid type in the rocks while drilling is usually straightforward with the use of gas and chromatographic measurements. Gas shows and oil shows while drilling are time-honored indicators of zones that need further investigation through logs, testers, and cores. In the rare case of gas-bearing, high-permeability rock drilled with high overbalance, gas will be flushed from the rock ahead of the bit, will not be circulated to the surface in the mud, and will not produce a gas show. Because hydrocarbons are not always part of a water-based-mud formulation, sophisticated analytical chemical techniques can be used on the oil and gas samples circulated to the surface and captured to determine the properties of hydrocarbons in a given zone penetrated by the drill bit.

Recent experience with a newly introduced sampling-while-drilling service has shown that it is possible to make reliable downhole formation-fluid-property estimates during sampling-while-drilling operations. These property and contamination estimates facilitate the management of the entire while-drilling sampling process by aiding sample-capture decisions and allowing the best possible use of the sample bottles currently available on a drilling bottomhole assembly. Moreover, the contamination estimates, together with the real-time fluid-property estimates, enable prediction of the uncontaminated-fluid properties. If performed during drilling (when filtrate invasion is not fully developed), fluid scanning--where formation fluid is pumped and analyzed within the sampling-while-drilling tool without taking a sample--affords the opportunity to characterize the formation fluid at potential sampling depths before committing sample chambers. The success of such an approach depends on how completely the in-situ fluid can be characterized and how free of contamination the assessed properties are. By use of downhole optical spectrometry, it has been shown recently that it is possible to estimate in real time at least the following formation-fluid properties: fluid color and type, hydrocarbon composition at various levels, carbon dioxide (CO2) content, gas/oil ratio (GOR), formation volume factor (FVF), and asphaltene content.

The SP curve is a continuous recording vs. depth of the electrical potential difference between a movable electrode in the borehole and a surface electrode.[1] Adjacent to shales, SP readings usually define a straight line known as the shale baseline. Next to permeable formations, the curve departs from the shale baseline; in thick permeable beds, these excursions reach a constant departure from the shale baseline, defining the "sand line." The deflection may be either to the left (negative) or to the right (positive), depending on the relative salinities of the formation water and the mud filtrate. If the formation-water salinity is greater than the mud-filtrate salinity (the more common case), the deflection is to the left.

ABSTRACT At reservoir conditions in a dry gas well, the fluid is outside of the pressure-temperature envelope and thus in a single phase. The reservoir temperature is above the cricondentherm (maximum temperature above which liquid cannot be formed regardless of pressure). Hence, the fluid can only be in a gaseous phase. Estimation of reservoir pressure in tight gas formations, such as unconventional, has been elusive and highly uncertain. Industry common practices analyze buildups and drawdowns with Pressure Transient Analysis (PTA) (Tongyi, 2014). An example of PTA is well testing. In tight gas reservoirs, however, this method is neither reliable nor accurate since a virgin formation will not yield enough gas due to its low permeability, typically in the order of micro or nanodarcys. Other methods include Rate Transient Analysis (RTA) or Dynamic Data Analysis (DDA), which consists of applying the fundamental flow-pressure relationships on production data, matching the responses to preexisting reservoir models with the use of type curves. This method is more robust than PTA, but requires substantial time to collect enough data points in tight gas formations. While drilling conventional formations, wellbore fluids (of density ρm) exert a hydrostatic pressure (Phyd) that is greater than the reservoir pressure (PRes) so mud filtrate (of density ρmf) invades the formation up to a radius of invasion (ri). Mud additives quickly create a pseudoimpermeable layer (mudcake) that prevents the filtration process to continue. While the formation effective porosity (φe) determines the extent of the ri for low- to high-permeable formations, it is the formation permeability (k) that controls the instantaneous or spurtinvasion process. Similar fluid dynamics occur while coring. The mud present in the borehole transmits the Phyd to the formation that is exposed by the core barrel while coring, which would tend to displace gas (of density φg) deeper into the formation. However, in extremely low permeability, and with a coring process lasting only minutes, the ri in the core is negligible so that the mud filtrates only cover ("paint") the core, but do not penetrate it. A tight gas formation is a system where the fluids do not flow or barely flow, and hence are static as opposed to dynamic. In addition, since in-situ water and rock compressibilities are easily calculated, the only change in volume is due to gas, which correlates with pressure and temperature downhole and in lab conditions. The current methods use dynamic measurements. This paper describes a static measurement of a static system. The method presented in this paper uses data from pressurized cores obtained downhole. The fundamental gas laws, P1V1T2=P2V2T1 and PV=ZNRT, together with mixing laws for density of fluids are used to calculate the original PRes in a dry gas-bearing tight formation. The method relies also on fluid and rock compressibilities and the estimation of porosity from wireline logs.

ABSTRACT Acquiring physical samples from an open hole is usually a one-opportunity event where a formation tester is sent downhole with a limited number of sample chambers, either on a logging-while-drilling (LWD) or wireline conveyance system. The samples are acquired, retrieved, and sent to a laboratory for analysis, which takes place weeks to months later. By the time the laboratory has performed an analysis, the section has been cemented, and perhaps the rig has finished operations and moved onto the next phase. Success of the sampling operation is predicated on the samples being acquired from the right locations (where to sample?), at the right time to minimize drilling fluid-filtrate contamination (when to sample?), and in a manner that preserves the integrity of the sample and is representative of the formation fluid (how to sample?). Digital sampling is a technique that that can be used to both optimize the when, where, and how of physical samples taken and further augment the information collected with sensor analysis from locations that are not physically sampled. This work shows a new workflow that can be used to extrapolate clean fluid properties with moderately high-contamination levels in a rapid pumpout. Based on the extrapolated clean fluid properties, an operator can make a decision whether to continue the pumpout to obtain physical samples or abort the pumpout if the fluid properties extrapolated (digital sampling) at the location are sufficient for the operation decision making. The workflow starts with applying principal component analysis (PCA) to a multichannel sensor measurement of fluid pumped out of the formation during a formation test sampling operation. Because the fluid pumped out contains only two endmembers (clean formation fluid and mud filtrate), the PCA scores of sensor measurements form a line in the PCA space, and solution bands of endmembers can be estimated based on physical constraint of sensor measurements (non-negative, etc.). Then, a trend-fitting method is used to predict the asymptote of the first principal component score. The asymptote value can be inverted to sensor signal using PCA inversion, and the sensor signal represents the clean formation-fluid measurement. Lastly, machine-learning-based composition models can be used to predict the clean fluid compositions based on the sensor signal. The composition data then is used to predict fluid physical properties, such as bubblepoint, viscosity, and compressibility, using an Equation of State (EOS) model. A series of rapid pumpouts at different depths can be used to map a formation for selection of where to sample, constrain contamination models to improve contamination estimation, determine when to sample, and optimize the pumpout parameters to obtain a representative sample in the shortest period of time. We have applied this workflow to a number of formation sampling jobs at multiple wells, the realtime results match with the laboratory analysis result in term of contamination level and clean fluid properties (compositions, GOR, bubblepoint, density, etc.)

ABSTRACT For every barrel of oil, about three to four barrels of water is produced. Water is part of every operation in upstream oil and gas: we produce it, we process it, we inject it. It affects our reserves because it may drive or sweep the oil out of the pores. It is a source of corrosion and scaling in pipe and in the reservoir. Measuring formation water resistivity (Rw) goes beyond using it as the basis of petrophysical well log interpretation. It is the key to telling different waters apart for taking the most representative samples. We introduce a calibrated induction-based water resistivity measurement sensor, which is configured to accurately measure Rw in the flowline of a formation testing tool. The induction-based operating principle of the sensor eliminates the use of electrodes and the associated fouling of the measurement due to coating or accumulation of particles on the electrodes. Instead, the sensor induces an electric current through a nonconductive, neutrally wetting flowline tube that is proportional to the conductivity of the fluid column within the tube. The resulting current at the receiver coil is then converted into resistivity. A case study presents data from a focused water-sampling station in a transition zone in a well drilled with water-based mud (WBM). The resistivity contrast between the mud filtrate and the formation water is low and mobile oil mixes with the formation water and mud filtrate. Despite these difficult conditions, the downhole measurement clearly shows the cleanup progress in real time and compares well with the surface measurements of the water samples. The ability to differentiate formation water from WBM filtrate with low resistivity contrast in the presence of oil places the station depth in the transition zone and enables accurate interpretation of contacts, saturation, and ultimately hydrocarbon in place. The sensor package is suitable for use up to 200-degC temperature and 35,000-psi pressure. The sensor can measure a wide range of resistivity, from 0.01 to 65 ohm.m. Measurements performed on known fluids prove its high accuracy of ±5% or less for resistivity below 10 ohm.m at a resolution of 0.001 ohm.m. The design eliminates any dead volume and all flowline fluid passes through the sensor. The sensor tube is smoothly flushable for fast dynamic response in multiphase slug flow. This paper also discusses optimal sensor placement and operational techniques to achieve best results in multiphase flow environments. The accuracy and resolution of the resistivity measurement enables direct comparison of guard and sample flowlines during focused sampling and provides differentiation even when the contrast between filtrate and formation water is low. The results can serve as a direct Rw measurement, for example in an exploration scenario, as successfully shown in another PDO trial, or can be compared to other sources of Rw measurement or used to improve the accuracy of alternatives to the Archie equation, such as dielectric dispersion.

For every barrel of oil, about three to four barrels of water is produced. Water is part of every operation in upstream oil and gas: we produce it, we process it, we inject it. It affects our reserves because it may drive or sweep the oil out of the pores. It is a source of corrosion and scaling in pipe and in the reservoir. Measuring formation water resistivity (R_w) goes beyond using it as the basis of petrophysical well log interpretation. It is the key to telling different waters apart for taking the most representative samples.

We introduce a calibrated induction-based water resistivity measurement sensor, which is configured to accurately measure R_w in the flowline of a formation testing tool. The induction-based operating principle of the sensor eliminates the use of electrodes and the associated fouling of the measurement due to coating or accumulation of particles on the electrodes. Instead, the sensor induces an electric current through a nonconductive, neutrally wetting flowline tube that is proportional to the conductivity of the fluid column within the tube. The resulting current at the receiver coil is then converted into resistivity.

A case study presents data from a focused water-sampling station in a transition zone in a well drilled with water-based mud (WBM). The resistivity contrast between the mud filtrate and the formation water is low and mobile oil mixes with the formation water and mud filtrate. Despite these difficult conditions, the downhole measurement clearly shows the cleanup progress in real time and compares well with the surface measurements of the water samples. The ability to differentiate formation water from WBM filtrate with low resistivity contrast in the presence of oil places the station depth in the transition zone and enables accurate interpretation of contacts, saturation, and ultimately hydrocarbon in place.

The sensor package is suitable for use up to 200-degC temperature and 35,000-psi pressure. The sensor can measure a wide range of resistivity, from 0.01 to 65 ohm.m. Measurements performed on known fluids prove its high accuracy of ±5% or less for resistivity below 10 ohm.m at a resolution of 0.001 ohm.m. The design eliminates any dead volume and all flowline fluid passes through the sensor. The sensor tube is smoothly flushable for fast dynamic response in multiphase slug flow.

This paper also discusses optimal sensor placement and operational techniques to achieve best results in multiphase flow environments.

The accuracy and resolution of the resistivity measurement enables direct comparison of guard and sample flowlines during focused sampling and provides differentiation even when the contrast between filtrate and formation water is low. The results can serve as a direct R_w measurement, for example in an exploration scenario, as successfully shown in another PDO trial, or can be compared to other sources of R_w measurement or used to improve the accuracy of alternatives to the Archie equation, such as dielectric dispersion.

The geochemical fingerprinting of produced water has been identified as a practical tool for operational applications in the petroleum industry. Provenance studies of produced water are essential to trace flow dynamics and reservoir compartmentalization in petroleum systems and to quantify fluid recovery rates from unconventional fracturing. Due to the fact that recovered oilfield water samples are frequently contaminated by operational fluids (i.e., oil-based mud, water-based mud, completion brines or stimulation fluids), representative samples for reservoir fluids have to be filtered from the geochemical data set of produced water. Besides the routine analysis of major elements (Na, Ca, Mg, K, Cl, SO₄, HCO₃), an enhanced geochemical monitoring program with selected minor and trace elements (i.e., B, Ba, Li, Sr), environmental isotopes (i.e., δ²H, δ¹⁸O, ⁸⁷Sr/⁸⁶Sr) and radiogenic isotopes (i.e., ³H, ¹⁴C) can provide in-depth information on the provenance of recovered oilfield water. Provenance studies of flowback water from hydraulic fracturing assets represent an enhanced method to assess the efficiency of the fracturing process by quantifying the recovered volume of originally injected fracturing fluid during the post-fracturing phase. The combination of gas recovery rates with geochemical flowback efficiency resulted in a practical tool to characterize the type and complexity of natural and induced fractures. Two cases studies showed that the combination of high gas recovery rates with low backflow efficiencies imply the presence of a complex system of natural and induced fractures. As a practical outcome, geochemical fingerprinting of recovered fluids can improve operational strategies for performed fracturing assets by avoiding water-pay zones, minimizing the amount of required fracturing fluids for injection purposes, and economizing the recycling process for recoverable flowback fluids. For the drilling of exploration or production wells, the presence of overpressured formations with a sudden water cut can frequently cause a technical challenge during well cementation. The design of a filtered geochemical database with regional fingerprints of formation water and groundwater zones is essential to identify the specific interval of water breakthrough for well plugging solutions. The routine geochemical analysis of reference water types, such as supply water and mud filtrate from the drilling process, is mandatory to quantify the potential flowback of applied drilling fluids.

Abstract Pertamina EP has recently drilled an exploration well in Akasia Maju Field located in the West Java Basin Area, Indonesia. The well penetrated the main reservoir over clastics play of Lower – Upper Cibulakan Formation. Recognizing these reservoirs from standard logging data (Gamma Ray, Neutron-Density and Resistivity) were very challenging, as they had the characteristics of tight, low contrast, low resistivity, limited to no neutron density crossover, and in some cases, high GR. Consequently, these reservoirs were easily missed on the previous exploration activity. Considering this situation, Pertamina EP has decided to change their wireline logging strategy, from the standard wireline logging suite to the combination of advanced wireline logging suite. This advanced combination was comprised of borehole resistivity image logs, nuclear magnetic resonance, and dielectric logs. These were mainly used to help identify potential reservoirs, optimize DST intervals, and distinguish between high water saturation and residual pay zones. In addition, the formation tester tool was run to measure formation pressures, obtain fluid samples, and determine fluid types and contacts. This combination has successfully revealed the presence of hydrocarbon zones on the intervals that had no clear indication of hydrocarbon occurrence in both sandstone and limestone zones of the Cibulakan Formation. In these hydrocarbon zones, the textural analysis of resistivity image logs indicates that they were correlated either with the well sorted rock fraction (in sandstone) or with the secondary porosity development (in limestone). Meanwhile, the nuclear magnetic resonance reveals potential reservoir candidates. The potential hydrocarbon zones were indicated by the prominence of specific porosity bins. Particularly, porosity bin number 6 (100-300 ms) and porosity bin number 7 (300-1000 ms). This is not usually the case, as hydrocarbon association cannot be determined using porosity bin distribution alone. The formation tester on Upper Cibulakan formation revealed the medium mobility zone in which pressure and fluid analysis data were acquired using the conventional formation tester. However, in the Lower Cibulakan formation, the mobility data showed very low to low mobility zone. Hence, the advanced formation tester was used to obtain pressure data and perform conclusive fluid analysis. Using this data combination, Pertamina EP managed to optimally select four DST intervals in Cibulakan Formation. The DST result was satisfactory, with the maximum oil rate reaching up to 1700 BOPD and the gas rate reaching up to 6.1 MMSCFD, positioning this well as a significant recent hydrocarbon discovery in Indonesia. From this case study, it can be concluded that the use of advanced wireline logging suite yielded definitive results. * Pertamina EP **Schlumberger

Abstract To develop scale management strategies and plans during field development planning, it is important to know the composition of formation water in the reservoir. Typically, formation water samples will be collected from appraisal wells and analysed for this purpose. However, when the wells are drilled with water-based mud, the samples are often contaminated with mud filtrate that has invaded the formation during drilling. By adding a tracer to the drilling mud and using a simple mass balance correction technique, it is possible to correct for the effects of contamination and obtain an estimate of the formation water composition. But, where reactions occur during invasion or within the sample after collection, this method of correction will generate an erroneous estimate of the composition. The errors will increase with the extent of reaction and degree of contamination. In this paper, we describe a new ‘correction’ approach which additionally makes use of (a) 1-D reactive transport modelling of mud filtrate invasion and (b) modelling of reactions occurring in formation water samples after collection. This approach accounts for the potential effects of these reactions and provides an estimate of the formation water composition within uncertainty limits. It reduces the risk of obtaining erroneous estimates of formation water composition and is particularly beneficial where reactions occur and where the mud contamination fractions are elevated (e.g. ~10-40%). At higher fractions, the uncertainties can be so high that the estimated compositions are not useful, emphasising the risks of trying to estimate formation water compositions from heavily contaminated samples. This approach has been applied to formation water samples obtained from the Nova Field (formerly Skarfjell, Norwegian North Sea). It has meant that the resulting composition and associated uncertainties have been used with more confidence in scale management planning; to select seawater as the injection water, and to identify the scale risks across the relevant nodes in the production process over the life of field of the asset. Based on these risks, appropriate scale mitigation and monitoring measures have been selected.