reza falahat

Objective of this study is to conduct a geochemical assessment over source candidates in the Garangan oilfield using Rock-Eval pyrolysis and vitrinite reflectance to identify the factors contributing to the lack of hydrocarbon reserves in the Middle Cretaceous – Early Oligocene petroleum system. Furthermore, the impact of mineral matrix retention, inert organic material (OM) and maturity were examined, and the results were used to split TOC and S2 parameters into the oil-prone and gas-prone components. Based on results, Pabdeh formation is an immature rich source, exhibiting the highest levels of active OM. Unexpectedly, OM quantity for the Kazhdumi source rocks is fair, for which contribution from inert OM is substantial. Significant inert OM is also confirmed for the oil/gas-prone upper Dariyan shales and gas-prone Gadvan samples. These characteristics seem to be related to the activities of the Khark-Mish paleo-high during the Cretaceous. Moreover, the Cretaceous intervals show a maturity level equivalent to the preliminary stages of main oil generation phase. Finally, owing to the presence of high-quality reservoir and cap strata, the absence of economical reserves in the Asmari and Bangestan reservoirs of the oilfield is possibly related to mediocre OM quantity and quality, and insufficient maturity of the source candidates.

In recent years, enhanced oil recovery (EOR) methods have received more attention because of the increase in demand and decrease in oil reserves. One of these methods is water injection. Due to the structural complexities and sedimentology of most of Iran’s reservoirs, the success of EOR methods, particularly water injection, is associated with high uncertainty. Therefore, monitoring and controlling the injection process using four-dimensional seismic surveys is necessary to reduce the operational risk and increase the production rate. In this study, the feasibility of seismic operation is investigated to monitor the water injection process in two reservoir formations of Burgan (sandstone) and Yamama (carbonate) in one of the oil fields in the south of Iran in zero, one, and three-dimensional scales. For this purpose, first, a comprehensive study was performed on the existing rock physics models, and the appropriate rock physics model covering most of the effective parameters was selected. Then, the changes in seismic response caused by the three scenarios of formation water injection, seawater injection, and smart water injection were investigated in each formation. The results obtained in the zero-dimensional scale showed that the acoustic impedance changes by 4.5-7% for different scenarios in both sandstone and carbonate reservoirs. To perform one-dimensional studies, synthetic seismic data were generated using well logs and for different scenarios of rising oil-water contact. The results of one-dimensional modeling indicate that four-dimensional seismic data can record any displacement over 3 meters in oil-water contact in the Burgan reservoir and over 7 meters in Yamama. To conduct studies on a 3D scale, 3D synthetic seismic data were generated using static and dynamic models of Burgan and Yamama reservoirs. Feasibility studies using 3D seismic data also indicate the possibility of success of 4D seismic data for monitoring changes in the fluid contacts of both Burgan and Yamama formations on a reservoir scale. Therefore, 4D seismic operation in the selected field is recommended for monitoring and optimizing the production plan and suggesting suitable locations for drilling new wells.

Source candidates for the Middle Cretaceous – Early Oligocene petroleum system in the Chilingar oilfield were geochemically evaluated using Rock-Eval pyrolysis and vitrinite reflectance measurements, along with taking matrix retention, inert OM and maturity effects into account. Moreover, the gas-oil ratio potential (GORP) factor was used to split the average live TOC and S2 parameters into the oil-prone and gas-prone constituents. Based on the results, Pabdeh and Gadvan formations have the highest and lowest hydrocarbon potential, respectively. According to the S2 vs. TOC diagram, the mineral retention effect and inert OM content are negligible for the Pabdeh formation. On the contrary, Kazhdumi and Gadvan samples show a significant mineral matrix effect and inert organic material, resulting in a substantial reduction of the hydrogen index and lower apparent quality. Based on the corrected-original parameters, Kazhdumi and Gadvan formations are categorized as mainly oil-prone and gas-prone source rocks, respectively. However, these formations show a low-to-fair quantity of live OM, due to the activities of the Khark-Mish paleo-high during the Cretaceous. In terms of thermal maturity, Pabdeh and Gurpi samples are immature, but Kazhdumi, Dariyan (the upper shaly layers) and Gadvan formations have entered into the preliminary stages of the main oil generation phase. Indeed, insufficient maturity of the source candidates (maximum Ro: 0.72) could be considered as a possible reason for the absence of hydrocarbon reserves in the Asmari and Bangestan reservoirs of the Chilingar oilfield.

Estimating and predicting pore pressure is essential to avoid drilling hazards in areas with high pore pressure. By predicting the pore pressure, this information can be used in  the wells planning, the optimum weight of drilling mud, finding the new exploration targets and etc. The main purpose of this study is to estimate the pore pressure using well logs and seismic data and  to compare their performance. In the well logging method, the pore pressure was estimated employing the relationships presented by Eaton in 1975 in three different ways that uses the resistivity, sonic and velocity logs. Then, in 1995, utilizing  Bowers equations, the overburden pressure was calculated from density log, and then the effective stress was estimated using the velocity relationship. Finally, the pore pressure was estimated using the Terzaghi relationship. In the next step, using three-dimensional seismic data and seismic inversion products the pore pressure was estimated in a three-dimensional cube employing Eton and Bowers methods. The estimated three-dimensional cubes were then assessed with the pressure data in the existing wells. The results of this study show that the Eaton velocity method with an error of 9.7% represent the good consistency with the measured data at the well location. It also provides the most appropriate spatial distribution of pressure at the reservoir and shallower depths. Therefore, Eaton velocity method is proposed on the similar regions for the pore pressure estimations.

Porosity is one of the important parameters in reserve estimation and development of a hydrocarbon reservoir. This petrophysical parameter is traditionally calculated from core and log data. The use of seismic data to estimate petrophysical parameters between wells has been of particular interest in oil and gas industry. In this study, seismic inversion was performed using two methods including model based and sparse spike using a combination of well data and post stack seismic data in an Iranian oil fields. The correlation and error of the sparse spike inversion method were 98 and 19%, respectively. However, the correlation and error of the model-based inversion were 88 and 47%, respectively. In the next step, porosity estimation was performed using three methods, including seismic multiple-attribute, probabilistic neural network and radial basic function neural network. The probabilistic neural network method provided 91% correlation between training data and 71% correlation between validation data that was a better answer than the other two methods. Therefore, it is suggested to use this method to estimate the porosity of seismic data in fields with similar geology.

Petrophysical reservoir properties are usually estimated from elastic properties such as acoustic impedance (AI) using petro-elastic models. AI data are only available at sparse well locations, especially in early exploration stages of oil fields and therefore to quantify the spatial reservoir properties, it is necessary to estimate the AI for the rest of the reservoir area. Alongside the deterministic seismic inversion methods, multi-realization geostatistical simulation approaches can be used for the parameter estimations and uncertainty quantification. Geostatistical simulation methods allow the production of two- or three dimensional models of reservoir properties using existing well log data. Conventionally, sequential Gaussian simulation (SGS) method is used because of its simplicity. However, its accuracy is not always guaranteed. Nowadays, turning bands simulation method (TBSim) has received much attention because of its capability to reproduce the statistical properties of the original data. The main principle behind the TBSim is simplifying a multi-dimensional geostatistical simulation problem into a set of fast 1D simulation problems. The simulations are performed along the uniformly distributed lines spanning a unit sphere using sinusoid functions.  In this study, we are going to generate 3D AI models in an oil field in SW of Iran via the TBSim. We will also compare the results with the AI data computed from seismic inversion. Geostatistical modeling has less computational complexity than seismic inversion. Although AI modeling cannot be used as a definite alternative to seismic inversion, it can be used as a primary method for estimating AI, especially in areas without seismic data. It also has the capability to be involved with some seismic inversion methods known as stochastic seismic inversion methods. The dataset used in this study includes AI logs of seven wells, one of which (known as test well) has been excluded from calculations to check the accuracy of the results. The study reservoir zone includes the Ahwaz sandstone member in the upper part of Asmari formation and also the lower carbonates. The lower carbonates of Asmari formation are separated by an unconformity with Jahrum formation. After constructing the structural model and upscaling the AI logs in it, 50 realizations of AI are generated in the gridded model. The results in the test well indicate a high correlation between the modeled values and real AI data as well as the model obtained by seismic inversion. The maximum correlation of AI values of different realizations with real values in the test well equals to 78.8%. The correlation coefficient achieves to 82.9 % for mean of realizations and is 72.8% for seismic inversion data. The cube of mean of realizations is also in good agreement with the seismic inversion cube and reproduces the dominant trend of vertical and lateral AI variations. Comparison of results histogram with real data as well as the seismic inversion data reveals the capability of geostatistical AI modeling in reproducing the statistical properties of original data. In addition, the results of the uncertainty analysis of produced models also confirm the reliability of these models, especially in the test well. Therefore, we would recommend the TBSim as a powerful method for AI modeling during reservoir characterization.

The instability of the wellbore has always been one of the major problems during drilling. In order to maintain the wellbore stability during drilling, the weight of drilling fluid is normally estimated to be decreased in the initial condition of reservoir. However, after reservoir production, the pore pressure of these layers decreases in case of having no pressure support. This change affects the magnitude and direction of in-situ stresses. In this situation, in order to maintain the wellbore stability for drilling new wells, it is necessary to consider the effect of reservoir pressure drop to determine the stability conditions and optimum well trajectory. Other factors affecting the stability of the wellbore are osmotic effect and drilling fluid temperature. In this research, one of the reservoirs in the Southwest of Iran was investigated. This reservoir underwent a pressure drop of 11 MPa during the production. The impact of reservoir pressure drop, osmotic effect and drilling fluid temperature are investigated on the wellbore stability condition and the optimum drilling trajectory. In the initial conditions of this reservoir, the minimum collapse pressure is in the range of 28.4–34.2 MPa, and the most stable drilling trajectory is a well with inclination of 45˚ in direction of minimum horizontal stress. After a pressure drop of 11 MPa that causes changes in induced stresses around the wellbore, the minimum collapse pressure for the stability decreases to the range of 21.7–26.4 MPa, and the most stable drilling trajectory, with 10˚ reduction in inclination, is a well with inclination of 35˚. In addition, including the effect of drilling fluid temperature, the minimum collapse pressure in the well is negligibly impacted to be in the range of 21.4 -26 MPa. Also, chemical effect of drilling fluid on the formation clay content increased minimum collapse pressure to the range of 22-26.7 MPa. Finally, considering both osmotic effect and fluid temperature estimates minimum collapse pressure in the range of 21.8-26.5 MPa. Therefore, the reservoir pressure drop has a significant effect on the wellbore stability condition, and other factors have negligible impacts on this reservoir.

Natural gas is accumulated in the reservoirs as either separate gas reservoir or the gas cap in an oil reservoir. Besides, gas is also injected into a hydrocarbon reservoir for IOR/EOR or gas storage purposes. Due to the reservoir heterogeneity or fault pattern in reservoir, gas could move to unplanned parts of the reservoir or could even be leaked, which in turn, deviates from the purpose of the gas injection. To overcome this problem and to monitor the fate of injected gas, 4D seismic data has recently been employed by oil and gas companies. 4D seismic, that is indeed, the repeated 3D seismic through the time has been recently revealed to be a successful tool for this purpose. However, there has been reported some challenges about the quantitative estimation of injected gas using 4D seismic data. The source of this challenge is mainly due to the non-linear response of elastic properties of saturated rock versus gas saturation. Once the gas is injected into core plug in the laboratory, the compressional velocity is significantly decreased for a few percents of gas saturation. Nonetheless, for higher gas saturation variation, not a considerable change is observed in compressional velocity. Because of this extremely non-linear behaviour, some researchers have concluded that the quantification of gas response is not possible using seismic data. In this research, it is tried to understand the reservoir scale gas distribution that is found to be different from the laboratory scale. Gas is migrated towards the upper part of the reservoir due to the gravity effect. It is quickly reached at a fixed gas saturation that is around maximum gas saturation (1-Swir). Continuation of gas injection increases gas thickness from top to base of reservoir, while gas saturation is practically fixed. Therefore, unlike the laboratory scale, the only variable on the reservoir scale would be the gas thickness, and not gas saturation. This is the key observation that would assist to understand proper 3D and 4D seismic response to injected gas. Two main 4D seismic attributes are chosen in this paper to understand those responses. The response of time shift and amplitude change were derived analytically and investigated numerically. The variety of reservoir models with different thickness and heterogeneities were made to analyze the seismic response. It can be concluded that for the medium to high-quality reservoirs, seismic response to the injected gas is simply linear; therefore, 4D seismic is still a powerful tool to quantitatively estimate the volume, distribution and migration path of the injected gas. It is proposed to continue this research to understand the seismic response on low quality (permeability and porosity) reservoirs.

One of the main objectives of 4D seismic interpretations is to estimate pressure and saturation change caused by reservoir production and injection. Estimation of these changes would assist to update the simulation and geomechanical models of our hydrocarbon reservoirs. Different techniques have been recently proposed to estimate the pressure and saturation changes using 4D seismic data. Typically, these methods linearly decompose the effect of pressure and saturation changes. For calibration of the proposed equations, laboratory measurements, rock physics relationships or even reservoir scale simulation model and well production data have been employed. Although, they work reasonably well in the given datasets, there is a need for extensive pre-setting steps to calibrate these equations which in turns requires time and cost. In this paper, Rock Physics and Petrophysics principles are utilised in order to develop two independent attributes which can calculate the pressure and saturation changes, separately. Both equations are easy to apply and interpret, and require not more than a few hours for their parameters calibration. Although, these two independent attributes were successfully implemented in one of the North Sea complex oil reservoir, but both attributes are qualitative indication of pressure and saturation changes.