Unlocking Potential in Thinly Laminated Reservoirs Through Cased Hole Pulsed Neutron Logging

Field A is located in the center of the Dnieper-Donets basin (DDB), producing gas from clastic reservoirs from several deep horizons in the Upper Visean sediments. The case study highlights the application of advanced pulsed neutron logging technologies and high-resolution data processing to unlock the sedimentary layers’ characteristics and the gas potential behind the casing. Multiple rock measurements are simultaneously recorded for continuous lithology identification, porosity quantification, and differentiating gas-filled porosity from low porosity formations. Dedicated log data acquisition and processing techniques enable investigating the effect of thin laminations on reservoir quality and producibility. The use of advanced pulsed neutron logging and interpretation method reduces the operational risks while securing critical reservoir parameters. A pulsed neutron spectroscopy tool provided a rich dataset including a self-compensated sigma and neutron porosity logs, fast neutron cross section (FNXS) together with capture and inelastic elemental spectroscopy. The logs interpretation was performed integrating FNXS and very high-resolution neutron porosity with mineral dry weight fractions and matrix properties from elemental spectroscopy processing. The comparison between the pulsed neutron measurements with standard open hole logs highlights the critical role of advanced fit-to-purpose logging techniques to accurately describe the underlying complexity of the formation and obtain improved net reservoir and net pay thicknesses in laminated and heterogeneous sequences. The logging objectives were successfully met, and additional valuable information related to the reservoir were determined in an efficient manner. The study also shows the critical value of FNXS as confident gas measurement. The FNXS measures the ability of the formation interacting with fast neutrons which are highly dependent on atomic density and not dominated by particular isotopes such as traditional sigma and porosity measurements. It is highly sensitive to gas-filled porosity, but it is independent of hydrogen index, acting like a cased-hole density measurement. Moreover, it demonstrates the importance of accurate knowledge of the mineralogy and matrix as well as the ability to measure at very high resolution to unravel the highly layered nature of the formation and its implication on completion and production strategy. Pulsed neutron logging has evolved over a half century, but the intrinsic physical measurements remain unchanged. With the advent and introduction of the new FNXS measurement and a high-quality spectroscopy elemental concentration, a higher quality measurement and interpretation can be obtained from standalone pulsed neutron logging. The advanced technology and log data analysis interpretation module can be considered as an effective and comprehensive methodology for robust formation evaluation in similar and complex setting.

Integrating Horizontal Wellbores When Building a Geological Model of an Offshore Field

With the advent of the equipment for full well logging suite in the horizontal wells, it became possible to evaluate the reservoir's quantitative parameters. However, the original curves are mainly used for this purpose, which leads to significant errors, in particular due to the significant influence of nearby reservoirs on the tools readings in the penetrated deposits. There is a need to discuss the current issues of interpretation in directional, horizontal and multi-lateral wells with the experts. 3DP module in the downhole software platform* allows to evaluate the overall influence of geometric effects, as well as to adjust logging curves for the influence of several reservoirs on the logging tools responses, which are not still taken into account by conventional methods when processing. The modeled density image is especially useful for confirming the model geometry, updating the local dip angle, and identifying areas, where additional features, such as thin layers, are to be added. The accounting for density and neutron porosity for layers in the petrophysical analysis increases the efficiency of calculating clay volume and porosity, which affects the saturation. The authors also proposed a methodology for assessing share of sand component based on RHOB image. Further accounting of NTG, for the correct assessment of the reservoir properties in a heterogeneous reservoir, followed by the data accounting in the geological model. The results obtained in the course of the work allowed to apply the spatial interpretation of horizontal well in geological modeling, as well as to improve the interpretation algorithm.

Methodology and algorithm to correct the thermal neutron porosity for the effect of rare elements and rock minerals with high neutron absorption probability

The thermal neutron porosity is routinely acquired in almost every well. When combined with the density, gamma ray and resistivity logs, the basic petrophysical parameters of a reservoir are evaluated. The design of the thermal neutron tool is simple, but its interpretation is complex and affected by the formation constituents. The most challenging situation occurs when the formation contains elements with high absorption probability of the thermal neutrons. The existence of such elements changes the neutron transport parameters and results in a false increase in the measured porosity. The problem is reported by many users throughout the years. In 1993, higher thermal neutron porosity is reported due to the existence of an iron-rich mineral, Siderite, in the Nazzazat and Baharia formations in Egypt. Siderite and all iron-rich minerals have high thermal neutrons absorption probability. Recently, in 2018, high thermal neutron porosity in Unayzah field in Saudi Arabia is also reported due to the existence of few parts per million of gadolinium. Gadolinium is a rare element that has high probability of thermal neutron absorption. Currently, none of the existing commercial petrophysics software(s) have modules to correct the thermal neutron porosity for such effects. This represents a challenge to the petrophysicists to properly calculate the actual reservoir porosity. In this paper, the effects of the rare elements and other minerals with high thermal neutron absorption probability on the thermal neutron porosity are discussed, and a correction methodology is developed and tested. The methodology is based on integrating the tool design and the physics of the neutron transport to perform the correction. The details of the correction steps and the correction algorithm are included, tested and applied in two fields.

Thermal Conductivity Estimation Based on Well Logging

The thermal conductivity of a stratum is a key factor to study the deep temperature distribution and the thermal structure of the basin. A huge expense of core sampling from boreholes, especially in offshore areas, makes it expensive to directly test stratum samples. Therefore, the use of well logging (the gamma-ray, the neutron porosity, and the temperature) to estimate the thermal conductivity of the samples obtained from boreholes could be a good alternative. In this study, we measured the thermal conductivity of 72 samples obtained from an offshore area as references. When the stratum is considered to be a shale–sand–fluid model, the thermal conductivity can be calculated based on the mixing models (the geometric mean and the square root mean). The contents of the shale and the sand were derived from the natural gamma-ray logs, and the content of the fluid (porosity) was derived from the neutron porosity logs. The temperature corrections of the thermal conductivity were performed for the solid component and the fluid component separately. By comparing with the measured data, the thermal conductivity predicted based on the square root model showed good consistency. This technique is low-cost and has great potential to be used as an application method to obtain the thermal conductivity for geothermal research.

AN ACCURATELY DETERMINING POROSITY METHOD FROM PULSED-NEUTRON ELEMENT LOGGING IN UNCONVENTIONAL RESERVOIRS

Unconventional reservoirs have low porosity and complex mineral composition containing quartz, feldspar, calcite, dolomite, pyrite and kerogen, which may seriously reduce the accuracy of the porosity measurement. The multi-detector pulsed neutron logging technique was already used for determining porosity through the combination of inelastic and capture gamma ray information in different spacing. In this paper, the new parameter, which is characterized by thermal neutron count ratio and lithology factor based on element content, is proposed to determine porosity from the three-detector pulsed neutron element logging in unconventional reservoir. To evaluate mineral composition, lithology, and gas/oil/water saturation in unconventional reservoir, a new multi-detector pulsed neutron logging tool was put out. The instrument consists of two He-3 thermal neutron detectors and a LaBr3 gamma detector. Therefore, the combination of thermal neutron count ratio between near detector and long detector with lithology factor of element content can measure neutron porosity and eliminate the influence of complex lithology. Based on some calibration pit data measured in laboratory, as well as the numerical simulation method, the influences of different lithological characters and mineral types on the neutron count ratio were studied. Meanwhile, large numbers of stratigraphic models with different lithological characters and different mineral compositions were established using Monte Carlo simulation method, and the content of silicon, calcium, hydrogen, oxygen, magnesium, aluminum and iron under different stratigraphic conditions was determined by the spectral element solution. A regression analysis was conducted to establish the relationship between the content of elements and the lithologic factor. The count ratio difference stemming from different lithological and mineral compositions was eliminated through a combination of lithological correction factor and thermal neutron count ratio. Different mineral compositions of stratigraphic simulation models were set up for verification. The absolute error of porosity measurement was less than 1.0p.u. in the formations with porosity less than 15p.u., which verified the accuracy of this method for neutron porosity evaluation in complex lithological characters formations. Two field examples were processed by this new parameter which in combination of thermal neutron count ratio and formation elements content information from the three-detector pulsed neutron instrument, which indicated a good accuracy for unconventional oil and gas reservoir evaluation.

RAPID CROSS-PLOT DISCRIMINATION OF COMMERCIAL POTASH MINERALIZATION – CASE HISTORIES

Potash minerals are a source of potassium, which is used for the manufacture of gunpowder and fertilizer. Commercial potash mineralization is often discovered when petroleum wells are drilled through evaporite sequences and the Gamma Ray log “goes off scale”. This is because potassium is one of the naturally occurring radioactive elements, emitting gamma rays from the 40K isotope, in its decay to 40Ar. However, not all potash minerals may be commercial sources of potassium via underground mechanical or solution mining techniques and Potassium is not the only radioactive element. For example, the mineralogy of the McNutt “Potash” member of the Salado Formation in SE New Mexico, is extremely complex, consisting of multiple thin (i.e., less than 10 ft thick) beds of six low-grade (radioactive) potash minerals, only two of which are commercial. There are also four non-radioactive evaporite minerals, one of which interferes with potash milling chemistry, and numerous claystones and Marker Beds (shales), with GR count rates comparable to the low-grade potash. Because of this complexity, traditional wireline and Logging While Drilling Potash Assay techniques, such as Gamma Ray log-to-core assay transforms, may not be sufficient to identify potentially commercial potash mineralization, for underground mining. Crain and Anderson (1966) and Hill (2019) developed linear programming, and multi-mineral analyses, respectively, to estimate Potash mineralogy and grades. However, both of these approaches require complete sets of multiple log measurements. In SE New Mexico, petroleum wells are drilled through the McNutt “Potash” member of the Salado Formation, with air, cased and drilled out to TD in the underlying sediments, with water based mud. Complete log suites are then run from TD to the casing shoe, with only the GR and neutron logs recorded through the cased evaporite sequence for stratigraphic and structural correlation. As a result, numerous oil and gas wells, in SE New Mexico, have cased hole gamma ray and neutron logs, through the Salado Evaporite. Logs, from these wells could provide a rapid Potash screening database, if used properly. A simple screening cross-plot technique, the Potash Identification (PID) plot, utilizing only Gamma Ray and Neutron Porosity, is proposed and successfully demonstrated, as a potential screening tool. This technique can be used with both open and cased-hole petroleum well logs, as well as core hole wire-line logs, and provides discrimination of commercial potash mineralization from non-commercial (potash and non-potash) radioactive mineralization. Case histories of the use of PID cross plots in the evaporite basins of Michigan, Nova Scotia, Saskatchewan, and SE New Mexico are described. The technique may also be useful in screening potential potash deposits in China, Europe, North Africa, and South America.

The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

Dolomitization geometry and reservoir quality from supervised Bayesian classification and probabilistic neural networks: Midland Basin Leonardian Wichita and Clear Fork Formations

Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian Basin. Early workers have found that the platform and shelf-top carbonates were dolomitized, whereas slope and basinal carbonates remained calcitic, proposing a reflux dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and although exhibiting high resolution vertically, such determinations are laterally sparse. We have used supervised Bayesian classification and probabilistic neural networks (PNN) on a 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity, we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well-log crossplots of neutron porosity and acoustic impedance to determine the a priori proportions of the lithology and the probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of the acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. The acoustic impedance volume was obtained by model-based poststack inversion, and the neutron porosity volume was obtained by the PNN. Our results best supported a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while the dolomitization is decreasing, but with sea-level forcing. With this study, we determined that diagenesis and the corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

DISCRETE FRACTURE NETWORK (DFN) MODELLING OF FRACTURED RESERVOIR BASED ON GEOMECHANICAL RESTORATION, A CASE STUDY IN THE SOUTH OF IRAN

Fractured reservoirs have always been of interest to many researchers because of their complexities and importance in the oil industry. The purpose of the current study is to model the fractured reservoir based on geomechanical restoration. Our target is the Arab Formation reservoir which is composed of seven limestone and dolomite layers, separated by thin anhydrite evaporate rock. First of all, in addition to the intensity, the dip, and the azimuth of the fractures, the magnitude and the direction of the stresses are determined using wireline data e.g. photoelectric absorption factor (PEF), sonic density, neutron porosity, a dipole shear sonic imager (DSI), a formation micro imager (FMI), and a modular formation dynamics tester (MDT). Then, the seismic data are interpreted and the appropriate seismic attributes are selected. One of our extracted attributes was the ant tracking attribute which is used for identifying large-scale fractures. Using this data, fractures and faults can be identified in the areas away from wells in different scales. Subsequently, the initial model of the reservoir is reconstructed. After that, the stress field and the distribution of fractures are obtained using the relationship between the stresses, the strains, and the elastic properties of the existing rocks. The model is finely approved using the azimuth and the intensity of fractures in the test well. Our findings showed that the discrete fracture network (DFN) model using geomechanical restoration was positively correlated with real reservoir conditions. Also, the spatial distribution of fractures was improved in comparison to the deterministic-stochastic DFN.

Study on the Origin and Fluid Identification of Low-Resistance Gas Reservoirs

The Wu 2 section of the Ke017 well block is a low-resistance gas reservoir with ultralow porosity and low permeability. The comprehensive analysis of rock lithology, physical properties, sedimentary characteristics, and gas content demonstrated that the development of micropores in illite/smectite dominated clay minerals together with the resulted additional conductivity capability and complex reservoir pore structures, as well as the enrichment of self-generating conductivity minerals like zeolites and pyrite which were the formation mechanisms of low-resistance gas layers in the Wu 2 section. A low-resistance gas reservoir has poor physical property, and it is difficult to distinguish the oil layer from the dry, gas, or water layers. In this paper, based on well/mud logging data and laboratory data, by taking advantages of the “excavation effect” of neutron gas and the dual-lateral resistivity difference between different depths, we successfully established a set of low-contrast log response methods for the identification and evaluation of oil layer and formation fluids. For a gas layer, the difference between neutron porosity and acoustic (or density) porosity is smaller than 0 and the difference in dual-lateral resistivity is greater than 0. For a water layer, the neutron porosity is similar to the acoustic (or density) porosity and the dual-lateral resistivity difference will be less than 0. While for a dry layer or a layer with both gas and water, the difference in porosity as well as dual-lateral resistivity is very small. The proposed method effectively solves the technical problem of oil layer and formation fluid identification in low-resistance gas reservoirs.