Prospects delineation and evaluation using the CDP gathers characterization, onshore Nile Delta, Egypt

Analysis of amplitude versus offset (AVO) variations in shallow reservoirs can be a powerful tool, and has been used to determine the nature and distribution of hydrocarbons in the Sole area.The Sole-1 well was drilled in 1973 on the northern margin of the Gippsland Basin. The well encountered a 16 m dry gas column at the top of the Latrobe Group, within a fault-bounded anticline.Seismic line GS88B-97 was acquired in 1988 over the crest of the Sole Anticline. A dipping reflection event cross-cutting the geological structure was interpreted as a reflection emanating from the base of the gas column. Structural interpretation is ambiguous at the field's northern bounding fault and potential was recognised for a significant extension of the field northwards.An AVO study comparing variations in measured reflection amplitudes on common depth point (CDP) gathers and substacks with modelled results has been successful in unambiguously delineating the field extent and shows that the field is bounded to the north by the main fault. This successful use of AVO techniques at Sole has permitted a more precise estimate of gas in place and has potential for application to other accumulations along the northern margin of the Gippsland Basin.

Amplitude variations with offset (AVO) analysis which is primarily applied to the prestack seismic gathers has been a topic of great interests as it is expected to provide us with more confident indication of hydrocarbon accumulation than simple investigation of the specific anomalies in the stacked seismic section does.AVO analysis was evaluated for potential indicator of gas sand reservoirs on the seismic data of an area onshore East Malaysia of which the stratigraphy is dominated by sand-shale sequence under deltaic environment of Tertiary age. There are two prospects of interests of which one has been confirmed that hydrocarbon be accumulated in a number of sandstone reservoirs and the other remains untested. AVO has been modeled at the tops of three gas reservoirs, which indicates significant increases in amplitude with offset (AVO anomalies) to be expected for all gas reservoirs.Seismic data was processed to be suitable for AVO analysis. Specific data conditioning and enhancement techniques to preserve amplitudes of the data were employed. These techniques include true amplitude FK filtering and time and space variant offset dependent residual amplitude correction.AVO was analyzed by investigating restricted angle stack sections and partially stacked angle traces of the processed data. Significant AVO anomalies were correlated with some of the tops of hydrocarbon reservoirs particularly gas reservoirs which exhibited considerably high amplitudes on stacked section. However, any significant AVO anomalies were not observed in the seismic data acquired on the untested structure.

The Halladale and Black Watch fields are adjacent fault-dependent gas accumulations at the Turonian Waarre Formation level situated in the eastern Otway Basin, about 4–5 km from shore in VIC/RL2(v). The two fields were first identified in 2002 when anomalous seismic amplitudes were observed on the tail-ends of several 90s-vintage 2D lines that extended into what was then vacant acreage. After being awarded the block as VIC/P37(v) Origin Energy Limited and its joint venture (JV) partner, Woodside Energy Limited, acquired a 211 km2 full-fold 3D seismic survey over the anomalous amplitudes in late 2003. Subsequent analysis of the seismic volume revealed two tilted fault blocks with strong amplitude variation with offset (AVO) anomalies in the Waarre A and Waarre C units that conformed to structure and appeared to shut off at the same depth. A similar AVO anomaly was also observed in the overlying Santonian Nullawarre Formation, raising the possibility that Halladale and/or Black Watch had leaked or were leaking. In early 2005, the VIC/P37(v) JV drilled two exploration wells targetting the key Waarre C reservoir on the eastern flank of Halladale and eastern crest of Black Watch. Both wells encountered live gas columns in the Waarre C but no GWCs were observed on logs and wireline pressure data indicated the two fields were not in pressure communication. A third well was then drilled down-dip of the Waarre C AVO shut off on the Halladale fault block to obtain a water gradient from the Waarre C. This well proved invaluable in determining the height of the gas columns in the Waarre C at both fields as it showed the gas-water contacts (GWCs) at Halladale (1,760 mSS) and Black Watch (1,770 mSS) were shallow to their respective AVO shut offs by about 20 m and 10 m respectively. Subsequent analysis of shear wave sonic data from the third well indicated there is a 17 m residual gas column at the base of the Halladale Field. This suggests Halladale either leaked slightly at some time in the past or is still leaking. A similar scenario may also occur at Black Watch. Given the close proximity of the two fields to the coast, development scenarios from onshore are now being considered.

The seismic reflection wave amplitude variation with offset (AVO) technique can effectively extract the lithology and fluid parameters that are hidden in the seismic record, and thus can be applied to gas hydrate prospecting and assessments in the permafrost region. Based on the geological conditions of pore-filling gas hydrate reservoir in Qilian mountain permafrost, the forward simulation velocity model of gas hydrate, by the rock physics model of the equivalent medium theory, was established. The AVO response characteristics of the gas hydrate reservoir with different properties by the forward modeling velocity model and the ray tracing theory were studied. The sensitivity of elastic parameters, for gas hydrate reservoir identification and formation lithology, was also studied. The AVO attribute partition changing with the porosity is quite well; the AVO attribute partition changing with the saturation presents continuous transition and could not be distinguished easily, and the AVO attribute partition changing with the thickness of the gas hydrate reservoir is linear. The μ and E are the most sensitive parameters to the properties of the gas hydrate reservoir, the crossplot of μ and E can effectively identify the gas hydrate reservoir, and the crossplot of μ and λ/μ could identify the lithology of the gas hydrate reservoir.

This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies. The entire seismic volume is classified into facies domains using machine learning, which isolates target features from seismic background data. The study area, covering 1850 km2, includes major structures such as the Rosetta fault and Nile Delta offshore anticline, with reservoirs consisting of layered sandstones and mudstones. Over 90 wells, including exploration and development wells, have been drilled in the area. Seismic amplitude data, including full and partial offset stacked, were analyzed to classify gas, water, and shale zones. The workflow’s performance is demonstrated through the successful identification of the low-amplitude Swan-E Messinian anomaly, characterized as a high-risk gas prospect. Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. By iterating over multiple attributes and validating the models on blind test sets and on a blind section, which excluded a known shallow gas field, the workflow significantly improved the ability to detect potential hydrocarbon reservoirs characterized by low seismic amplitude. The results show that this integrated approach reduces exploration risk, quantifies the chance of success, and enhances decision-making in well placement and hydrocarbon exploration. This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.

The Kerna gas field is located in the south-central part of the Cooper Basin close to the large Dullingari and Toolachee Fields. Kerna is a domal anticline and contains gas structurally trapped within sandstones of the Early Permian Patchawarra Formation. The overlying Epsilon Formation sands also contain gas which may be stratigraphically trapped around the south and west flanks of the field.Seismic reflection amplitudes are used to map the extent of this Epsilon gas sand. Seismic modelling studies show that the gas sand displays an amplitude-versus-offset (AVO) response which distinguishes it from a wet sand or from a coal bed at the same stratigraphic level. Constructive and destructive interference from the top and base of the gas sand, and from overlying coal seams, have a marked effect on the overall seismic stack response of the target horizon. The modelled AVO response shows a good correlation with AVO changes on cd.p. gathers from seismic lines across the area.The spatial distribution of the AVO anomalies (AVO increases), and of the overall seismic stack response, has been mapped across the field and can be used to locate future appraisal and exploration wells. This study has implications for other areas of the Cooper Basin where adequate separation between coal beds and gas sands allows the AVO effect of the latter to be observed. These AVO effects can then be used as a direct indicator of gas in stratigraphic or even structural traps.

This paper deals with the application of well-established pressure transient analysis techniques to the determination of geothermal reservoir parameters. Among the pressure transient techniques available, those concerned with two-rate flow testing were chosen. A two-rate test may permit obtaining data while reducing interruption of power generation. Two-rate techniques have been applied successfully to both oil and gas reservoirs; however, no data have been published to date—to the authors' knowledge—on the application of this method to liquid-dominated geothermal reservoirs. Data from one test run on a well in the Cerro Prieto Geothermal Field are shown. The field data were interpreted by means of four different models. Three of the models produced results that agreed with each other, the fourth one produced data scatter. Introduction The Cerro Prieto Geothermal Field is located about 30 kilometers south of Mexicali, Baja California. As shown in Figure 1, this field is situated at the southern end of the Salton-Mexicali trough, which includes other thermal anomalies of great interest, such as Heber and East Mesa. The reservoir is a liquid-dominated system having a cap rock made of impervious plastic clays. This cap acts as a seal, keeping the hot water trapped and preventing the dissipation of heat to the surface. Figure 2 shows a schematic geological cross section of the reservoir drawn in an East-West direction. The permeable layers consist of alternating shale and permeable layers consist of alternating shale and sandstone layers resting on a highly fractured granitic basement. Basement rocks were encountered in one of the wells at a depth of approximately 2500 meters. The thickness of the cap rock varies, according to the location of the wells, from 700 up to almost 1000 meters in the portion of the field already drilled. The first exploratory well in the area was drilled in 1961, and in 1964, four more exploratory wells were drilled. After an extensive field-test program, the Comision Federal de Electricidad program, the Comision Federal de Electricidad started the construction of a 75-MW geothermal power plant in 1968. The plant was located in the Mexicali plant in 1968. The plant was located in the Mexicali valley and named for the Cerro Prieto volcano. This plant started commercial operation in April 1973, and has been in operation since that date. The turbines operate on steam from 14 wells. The geothermal fluid is obtained as a water-vapor mixture, because flashing takes place at some depth inside the wellbore. The separated hot brine is disposed of in an evaporation pond; however, plans are being made to evaluate the feasibility of reinjecting at least part of the spent liquid. Although well testing and pressure transient analysis techniques have been widely applied by petroleum engineers to gas and oil reservoirs (to petroleum engineers to gas and oil reservoirs (to find mean formation pressure, skin factor, and average permeability and porosity), the application of the permeability and porosity), the application of the same techniques to geothermal reservoirs encounters problems that are not commonly found in petroleum problems that are not commonly found in petroleum applications. In hot-water wells, two-phase flow and heat transfer influence the pressure response when the wells are shut-in. In addition to this, gathering of the data is difficult because of the high temperatures involved. Bottom-hole temperatures in the range of 300 to 320 degrees C are common in the Cerro Prieto Field. Until recently, there were no bottomhole pressure measuring devices that could withstand such temperatures for more than three hours. Another problem is mechanical damage due to extreme buffeting problem is mechanical damage due to extreme buffeting by the high production rates of boiling geothermal fluids. Rates in excess of 24,000 B/D are common. Considering these facts, it was decided that the best choice for obtaining pressure-time data by means of standard bourdon-tube type pressure bombs would be short-time duration drawdown tests. These tests could be run by changing the flow rate to some predetermined value after the well was stabilized at a predetermined value after the well was stabilized at a given constant rate for some time. This type of test is known in petroleum technology as a "two-rate" flow test. It was first proposed by Russell in 1962. Further improvements in this technique were introduced by Selim and Odeh and Jones.

The Common Depth Point (CDP) Method is a seismic data acquisition and processing technique which transforms field recordings from seismic surveys into pseudo-cross-sectional images of the earth's geologic layering beneath the survey line. Such images are the primary geophysical analysis tools used by explorationists to pinpoint likely drilling locations for oil and gas reservoirs. The CDP Method has evolved from an elementary data enhancement concept in the analog seismic era of the 1950s to a highly sophisticated digital imaging and parameter estimation technique in the 1980s. This paper reviews the history of the method, and its underlying mathematical-physical basis in terms of simple computer simulations of reflection seismic experiments. The method is illustrated with actual examples from several geologic provinces, and the paper closes with a discussion of some limitations of the CDP Stack and possible future directions in seismic imagery.

In the late 1960s, several oil companies noticed that, in environments of young clastic sediments, large seismic amplitudes were associated with gas‐saturated sands. This method of correlating lithology to normal incidence (NI) reflectivities was appropriately named the bright spot technique. However, it quickly became apparent that not all large amplitudes were necessarily associated with gas reservoirs and, much to our chagrin, not all gas reservoirs had large amplitudes. For the next decade, geophysicists tried numerous techniques to resolve the ambiguity associated with lithologic identification by means of the seismic attribute NI. This met with various degrees of success. Finally, Ostrander’s work on amplitude variation with offset (AVO) led to the development of another seismic attribute(s) which improved our discrimination of lithologies (see “Plane‐wave reflection coefficients for gas sands at nonnormal angles of incidence,” Geophysics 1984). But proper application of this new attribute along wit...

Fenchuganj Gas Field is located in the Surma Basin of Bangladesh and characterized by water-drive gas field. In the reservoir condition, water saturation increases as gas production rise. The fluid properties of the four individual gas zones of this reservoir at the present condition and at the gas depleted condition should be addressed with proper prediction. In this paper, we characterize the total reservoir with special emphasis on Upper Gas Zone and New Gas Zone I which are compared with other two gas zones (New Gas Zone III and New Gas Zone II) representing some modeling results (has done before by these authors) which evidences that the pore fluids have a significant effect on the acoustic impedance and the Poisson’s ratio of the reservoir rock which is directly correlated with seismic amplitudes at constant pressure with Batzle-Wang model and Gassman-Boit models. These models with varying pressure and water saturation conditions show the reasonable predicted fluid modulus against pressure for all four gas sands. The reservoir modeling from irreducible water saturation condition (90% gas saturation) to residual gas condition (10% gas saturation) provides a way to estimate values at reservoir conditions from logging conditions. Fluid bulk density increases when water saturation increases with constant pressure and stay around constant when water saturation increases with pressure drop. But overall it increases through the production path that we assumed. Amplitude versus Offset (AVO) analysis is also compared with other study models which show that seismic reflection of p-wave changes due to change of pressure and water saturation of the reservoir rock layers. This study is also showing that all four gas zones of the Fenchuganj are under gas sand category 3. We propose the modeling of fluid property in determining the convenience of time lapse seismic, predicting AVO and amplitude response, and forecasting in the study field and making production and reservoir engineering decisions.

A three-dimensional, single-phase reservoir simulation model of the entire Gippsland Basin aquifer system, together with its oil and gas reservoirs, was first developed in 1973. It was replaced by an improved version in 1975. Now, after fifteen years of production, pressure predictions from the model still compare very well with data obtained from current exploration and development wells.The model, consisting of 4186 grid blocks, incorporates the geological description, and pressure and fluid distribution of the basin. The geological description includes porosity, net-to-gross ratio and permeability with fluid properties representing the aquifer. A well-established initial pressure/depth relationship for the Gippsland Basin is included in the model. Although it is a single-phase model, oil and gas reservoirs are represented by pseudo rock and fluid properties.The model is regularly updated with historical and forecast production rates in order to predict pressure behaviour and therefore aquifer strength in various areal and stratigraphic locations in the basin. Such information is essential for defining external boundary conditions in individual reservoir simulation models and assists in gas deliverability forecasts. In exploration wells, measured pressures are compared with model predictions to help understand the degree of pressure communication with the basin aquifer and hence the level of pressure support. Detailed predictions of the pressure gradients expected in both exploration and development wells are often of assistance in identifying fluid contacts, overpressure and reservoirs with limited communication with the aquifer.

Correlation and difference between conventional and unconventional reservoirs and their unified genetic classification

Hydrocarbon accumulation depth limit and implications for potential resources prediction

Reflection P- and S-wave data were used in an investigation to determine the relative merits and strengths of these two data sets to characterize a naturally fractured gas reservoir in the Tertiary Upper Green River formation. The objective is to evaluate the viability of P-wave seismic to detect the presence of gas‐filled fractures, estimate fracture density and orientation, and compare the results with estimates obtained from the S-wave data. The P-wave response to vertical fractures must be evaluated at different source‐receiver azimuths (travelpaths) relative to fracture strike. Two perpendicular lines of multicomponent reflection data were acquired approximately parallel and normal to the dominant strike of Upper Green River fractures as obtained from outcrop, core analysis, and borehole image logs. The P-wave amplitude response is extracted from prestack amplitude variation with offset (AVO) analysis, which is compared to isotropic‐model AVO responses of gas sand versus brine sand in the Upper Green River. A nine‐component vertical seismic profile (VSP) was also obtained for calibration of S-wave reflections with P-wave reflections, and support of reflection S-wave results. The direction of the fast (S1) shear‐wave component from the reflection data and the VSP coincides with the northwest orientation of Upper Green River fractures, and the direction of maximum horizontal in‐situ stress as determined from borehole ellipticity logs. Significant differences were observed in the P-wave AVO gradient measured parallel and perpendicular to the orientation of Upper Green River fractures. Positive AVO gradients were associated with gas‐producing fractured intervals for propagation normal to fractures. AVO gradients measured normal to fractures at known waterwet zones were near zero or negative. A proportional relationship was observed between the azimuthal variation of the P-wave AVO gradient as measured at the tops of fractured intervals, and the fractional difference between the vertical traveltimes of split S-waves (the “S-wave anisotropy”) of the intervals.

Shale anisotropy must be quantified to obtain reliable information about reservoir fluids, lithology, and pore pressure from seismic data. Failure to account for anisotropy in seismic processing can lead to errors in normal moveout (NMO), dip moveout (DMO), migration, time-to-depth conversion, and amplitude variation with offset (AVO) analysis. Kriged estimates of density, vertical compressional-wave, and shear-wave velocities were derived from vertical wells in an area of interest in the Horn River resource play in northeastern British Columbia. The kriged predictions were compared with measured logs along the trajectory of a deviated well. Whereas the density comparison provided a blind test of kriging accuracy because density is scalar and independent of well deviation, the same comparison for sonic velocities revealed that they are systematically higher than the kriged vertical velocities. This difference was used to estimate anisotropy parameters at the location of the deviated well. The fact that the higher velocities observed are caused by anisotropy was confirmed subsequently by using the derived anisotropy parameters to apply a nonhyperbolic moveout correction η to flatten gathers from a seismic survey 10 km north of the area of interest, within which the anisotropy parameters were estimated.