Moharana, Abhishek (Schlumberger) | Mahapatra, Mahabir Prasad (Schlumberger) | Chakraborty, Subrata Kumar (Schlumberger) | Biswal, Debakanta (Adani Welspun Exploration Limited) | Havelia, Khushboo (Schlumberger)
Petroleum Geologists have always been a group who looked at rocks, developed and described depositional concepts, mapping structures to discover and develop hydrocarbons for profit. With the advent of new technologies and computing power, geology started to become a lot more quantitative. The first wave of this new revolution was the introduction of geostatistics and the discipline of geomodelling, dealing with quantitative statistics like variograms, histograms, stochastic models which could be used to put a number and range on the geological uncertainty. However, geostatistics which was originally developed in the mining industry in the 1950's deals more with regularly sampled data, describing their spatial variability and directionality. In majority of development fields, with many wells sampling the reservoir, geostatistics helps us to create a feasible proxy for the subsurface reservoirs, when it is backed by a strong conceptual geological foundation. However, as the number of wells decreases, the data for geostatistical analysis reduces and a geomodeller must rely strongly on the conceptual geological knowledge, to build a predictive geological model rather than the noisy picture which over-reliance on blind geostatistics can provide. Until recently, there was no way of quantifying or visualizing depositional concepts in 3D for a geologist save for few block diagrams and average sand distribution maps. However, these were mostly manual, deterministic with a long turnaround time for any alternate concepts.
A relatively recent and still underused addition to the geologist's set of quantitative tools has been geologic process modeling (or GPM, also called stratigraphic forward modeling). This technique aims to model the processes of erosion, transport and deposition of clastic sediments, as well as carbonate growth and redistribution on the basis of quantitative deterministic physical principles (
In the currents study a 3D geological model for the B-9 field, based on the Geological Process Modeling (GPM) has been attempted Owing to the thin pays in deltaic sands, understanding reservoir continuity from seismic data was not possible. With only 4 wells available in the field, traditional geostatistics based facies models were inadequate in explaining the reservoir distribution. Thus, a combination of Stratigraphic Forward Modeling with Multi Point Statistics is used to accurately capture sub-surface facies heterogeneity.
The Mishrif formation in Abu Dhabi comprises progradational shelf margin facies. The western platform sediments are characterized by stacked clinoforms of clean high energy carbonates, with generally good reservoir properties at the top deteriorating gradually toward the west flank. In contrast in the east the formation is thicker and characterized by more differentiated, coarsening, shoaling- upwards sequences. High quality reservoir facies occur only near the prograding shelf edges.
The reservoir in this study had finely layered pillar grid models of the field using onlap and offlap layering to capture the vertical property heterogeneity and layering within the clinoforms implied by the depositional environment. However, this grid structure posed challenges for flow simulation as there were unphysical barriers and connections across the clinoform boundaries caused by the pinching out layers at boundaries between clinoform units.
By construction, in a depogrid each clinoform may be independently gridded with coordinate lines that do not need to be continuous through the vertical extent of the reservoir. Layers within a sequence can truncate arbitrarily against bounding discontinuities since the cells are polyhedral and the grid globally unstructured. Therefore, an evaluation of the depogrid cut-cell grid was undertaken for the reservoir.
A volume-based model was constructed using horizon surfaces and fault surfaces extracted from the pillar grid model, and the depospace transform calculated. The depogrid was created using the same areal resolution as the pillar grid. The layer parameters were set to give approximately the same number of layers in each zone as the pillar grid. The rock type base properties from the pillar grid were upscaled onto the depogrid, in the interest of time, to populate the static depogrid model. Reservoir fluid properties, relative permeability, and capillary pressure curves were taken from existing simulation models of the field. A high-resolution reservoir simulator was then run on the depogrid model. Run time comparisons of the pillar and depogrid simulations were made
The evaluation concluded that the cut-cell stratigraphically layered depogrid provided a more geologically consistent representation of the complex stacked clinoform structural elements and required significantly fewer grid layers to achieve the required vertical resolution. The depogrid simulations could represent the expected connectivity for flow. Improved run times were observed
Mauddud Formation is a major oil-producing reservoir in Raudhatain Field of North Kuwait. The Mauddud Formation is an early Albian in age and it was generated an environment of the shallow-water carbonate and consists of Grainstones, Wackestones and Mudstones deposited in ramp settings. In Raudhatain field (RAMA) is undertaking massive development efforts with planned enhancement in Oil production. Reservoir description and distribution of rock properties in 3D space are challenging due to inherent reservoir heterogeneity, in this case primarily driven by depositional and diagenetic patterns.
KOC North Kuwait Reservoir Studies Team (NK RST) has been challenged to increase the production from several key NK oil fields. To achieve this goal, KOC has partnered with Schlumberger to rebuild integrated model with Petrophysics, Geophysics, and Geology and Reservoir data of the Mauddud Reservoir. The original model was required to minimize challenges in new infill locations, increase Oil recovery factor and detect water breakthrough to minimize water production. One of the key issues in creating RAMA reservoir model is integration of all available data in identifying the horizontal permeability, reservoir heterogeneity and identification of thief zones.
A fine Geological grid model with 35M cells, 10 Geological horizons has been built to characterize the Mauddud reservoirs of the RAMA field including the permeability from PLT logs combined with petrophysical and lithological / facies data to add more understanding of the distribution of reservoir properties. Log response group methodology and the undeveloped area in the Saddle (structurally low area) has been modelled for the first time in Raudhatain NK Field. This combined study utilizes the available data and cutting-edge technology using Geo2Flow which resulted in fluid compartmentalization and free water level identification. STOOIP has been upgraded and unlocking potential in new segments of the developed field. The original model was built based on vertical/Deviation wells (345) which lead to discrepancies in the structural interpretation. The new update has been carried out including all horizontal wells to minimize the uncertainty in the structure framework.
Dhote, Prashant (Kuwait Oil Company) | Al-Adwani, Talal (Kuwait Oil Company) | Al-Bahar, Mohammad (Kuwait Oil Company) | Al-Otaibi, Ahmad (Kuwait Oil Company) | Chakraborty, Subrata (Schlumberger) | Stojic, Slobodan (Schlumberger)
Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics.
Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences.
In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'.
The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model.
The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.
Amer, Aimen (Schlumberger) | Al-Wadi, Meshal (Kuwait Oil Company) | Salem, Hanan (Kuwait Oil Company) | Sajer, Abdulazziz (Kuwait Oil Company) | Al-Hajeri, Mubarak (Kuwait Oil Company) | Najem, Ali (Schlumberger)
Outcrop work represents the main source of analogs used to model subsurface reservoirs. Without such explanation of reservoir geometry, architecture, and characterization, producing subsurface formations would be largely uncertain. The aim of this paper is to build a geological static model for the Enjefa Beach outcrop exposed in Kuwait and use it to better understand subsurface reservoir architectures. This was achieved by acquiring several traverses along the outcrop, describing the various rock units, and understanding the depositional facies and facies associations. The next stage was to model each depositional unit as a separate zone embedded in an integrated model. This was followed by developing a forward synthetic three-dimensional seismic model to better understand how such reservoir architecture may appear in the subsurface. The final step was to use these findings in modeling a subsurface Cretaceous reservoir in northeastern Kuwait. The resultant model demonstrated that detailed geological complexities can be captured by conventional modeling techniques; in the model, the middle shoreface, upper shoreface, foreshore, and tidal channel complexes were statically modeled. Subsurface seismic data showed a series of highly sinuous meandering channels. Stacking patterns were found to vary among vertical, climbing, and compensational stacking patterns.
Han, Rubing (Research Institute of Petroleum Exploration and Development, Petrochina) | Li, Shunming (Research Institute of Petroleum Exploration and Development, Petrochina) | Song, Benbiao (Research Institute of Petroleum Exploration and Development, Petrochina) | Tian, Changbing (Research Institute of Petroleum Exploration and Development, Petrochina)
The giant H oil field was discovered in 1976 and put into development in 2008. The Mishrif reservoir has half of the geological reserves, and the current development challenges are greater. First, the sedimentary evolution is very complicated (inner ramp, lagoon, shallow sea, et al), the existing facies model is generally derived from rock type interpolation with kriging algorithm, and does not reflect sedimentary understanding, and is quite different from the actual situation displayed by seismic data. Furthermore, the variogram of rock types is also difficult to be accurate, resulting in poor fitting of reservoir simulation. Second, the seismic data quality is generally low, and its correlation with rock types is worse. If it is directly used to constrain rock type interpolation, the accuracy is generally unsatisfactory.
In order to solve these problems, this study first comprehensively studied the core, thin slices, scanning electron microscopy, X-ray diffraction, electron probe and other data for sequence stratigraphy and sedimentary research. Secondly, based on the single well facies interpretation, combined with the inversion data, the dimension, spatial relationship and variogram of each depositional element were studied. Thirdly, the facies model was built using the Sequential Indicator Simulation (SIS) algorithm, and was manually modified zone by zone according to the sequence stratigraphy and sedimentary understanding. Finally, the relationship between depositional elements and rock types was studied, and the probability bodies of different depositional elements with different rock types were obtained. Based on this, the SIS algorithm was used to simulate rock types. Then this could be further used as a constraint to build petrophysical models.
The results showed that the correlation between the depositional elements and the rock type was obviously better than the inversion wave impedance. The artificially modified facies model could better reflect sedimentary appearance, and various static and dynamic data. The rock type model obtained under the constraint of the facies model also bypassed the problem that the seismic data quality was not good enough for direct rock type interpolation.
It was verified that with the new modeling workflow, the preliminary numerical simulation fitting rate reach over 80%. The facies model was then verified with water flooding test in H oil field and had good results. This workflow could provide a good reference for similar oilfields in the Middle East.
Saleh, Ahmed (Safer Exploration & Production Company) | Al Maktari, Amin (Safer Exploration & Production Company) | Alogily, Abdulkhalek (Safer Exploration & Production Company) | Haygana, Adel (Safer Exploration & Production Company) | Al Adhashi, Jaber (Safer Exploration & Production Company) | Ahmad, Ammar (Schlumberger) | Khan, Waqar A. (Schlumberger) | Baig, Muhammad Zeeshan (Schlumberger) | Jibran, Ali (Schlumberger)
The objectives of this work were to achieve realistic reservoir modeling using seismic inversion volumes, advance petrophysical & rock typing analysis for predictive modeling of reservoir quality sands of Lam Formation. Generation of predictive scenarios for the constructed reservoir model for reduction in uncertainty. Identification of new infill well locations based on the predictive reservoir sand distribution. Reevaluation of in place volumetrics for Lam Formation in Prospect D Field. The methodology adopted to achieve the above results.
The methodology for this work contained the following steps; In the first step seismic inversion was performed on Prospect D Field 3D seismic dataset to obtain volume of clay seismic volumes and facies volume. Petrophysical rock typing combined with the core data analysis was sued to calibrate the inverted volumes by identification of clay typing. The seismic inversion volumes were integrated with the depositional settings of Lam Formation to qualitatively interpret the inverted volumes. Reservoir modeling was performed using seismic inversion facies volumes and petrophysical rock type model to predict and distribute the different depositional facies being controlled by inversion trends. Generation of In place volumes and predictive scenarios for reduction in uncertainty and attributing more predictive strength to the reservoir model.
On the basis of reservoir modeling two (2) prospective areas were observed to show good quality sand bodies which were non-tight and reflected good reservoir properties. The seismic inversion volumes captured the depositional trends within the Lam Formation showing the variation between channel complexes to reservoir quality delta sand bodies. Two (2) to Four (4) infill well locations were identified along with forecasted results which showed positive results based on the delineated prospective areas. The seismic inversion volume results, petrophysical rock typing combined with core data completely changed the field development plan by identifying new prospective areas which were not identified or interpreted previously.
Conceptual limitations of existing gridding technologies often lead to undesirable simplifications to the modeling of structurally complex areas, and consequently poor predictions. We present a structural modeling and gridding workflow that limits these modeling compromises.
A volume-based 3D structural model based on fault and horizon surfaces is constructed from input data that has undergone basic quality checking using a variety of techniques. The critical step in the grid creation is the definition of a flattened (‘depositional’) space that deforms the structural model mesh under mechanical constraints. A 3D ‘unstructured’ grid is created in the depositional space, based on ‘cutting’ a property-populated, regular cuboidal grid by the geological discontinuities. The tectonic consistency and better preservation of geodetic distance make the flattened space ideal for a range of property modeling approaches. The forward-deformation of the grid into true geological space tends to preserve the layer-orthogonality of the grid columns and makes the grid more suited to numerical simulation approximations. The final grid is unstructured, high quality and an accurate representation of the input structural model.
The 3D structural model, depositional space transform and grid geometries all provide valuable information on the structural quality of the input data. The stretching and deforming of the orthogonal local axes in the transformation from depositional space to geological space are used to focus further effort on structural model quality assurance (QA). The key step in generating accurate property population and simulation models is the application of QA metrics on the grid geometry; the transformation from depositional space to geological space is used to generate a set of grid properties that highlight potential structural inconsistencies or data quality issues back in the structural model. We present several examples based on a range of structurally complex models, and demonstrate the downstream impact of applying this QA workflow throughout the stages of input data validation, structural model creation and grid creation.
In recent years, Carbon capture and storage technology (CCS) has been recognized as one of the potential methods to reduce greenhouse gas emission and for mitigating global climate change. This practice can be done in the depleted reservoir as well as saline aquifer reservoir. Geological modeling is an important process to prove the suitable geologic formation in CCS project. The CO2 plume behavior depends on geology structure of storage formation. This study focuses on geological modeling and the simulation of CO2 plume behavior in saline sandstone of fluvial deposit, Nam Vang field, Cuu Long Basin, Vietnam. Channel sand and floodplain are defined based on well-logging data. Fluvial facies are distributed in the three-dimensional grid by using the object-based method with consideration of lateral continuity, vertical range and orientation in each facies. The porosity and permeability are modeled stochastically to conditioning to facies. The advantage of object-based modeling constrained the petrophysical model to facies model to assign the high porosities and permeability distributed within channel sand-dominated facies. The low porosities and permeability populated within floodplain-dominated facies. CO2injections were simulated using ECLIPSE300-CO2STORE. Sensitivity analysis has been conducted to investigate the behavior of CO2 plume for reservoir saline sandstone. Simulation results indicate the extent of CO2 plume dynamic is sensitivity to the geometry and sinuosity of the fluvial channel. The object-based modeling can construct the geological model to relate with fluvial channel facies correctly. This method is used to support for geological CO2 storage modeling in the fluvial deposit. As a general evaluation, this study can contribute to CO2 storage in an offshore area in Vietnam.
Reservoir modeling and the derived fluid production over time curves are a key part of the workflows associated with major capital project decisions. These models may be very complex and use a variety of geological constraints in an effort to develop the porosity, permeability, and saturation distributions used in dynamic models (with or without upscaling). Over time and partially in response to increased computing capability as well as the need for more realistically heterogeneous models, model size as measured by number of model cells and model complexity has increased but model-derived production forecasts remain optimistic. This paper, one of a series that now stretches back over a decade, addresses a number of modeling issues with the goal of (1) better understanding how modeling workflows may contribute to forecast optimism and (2) what reservoir modelers, both geologists and engineers, may do to reduce forecast optimism derived from their subsurface models by improved understanding of how model parameters such as grid size, number of grid cells, semivariogram parameters (e.g. the range), and number of geological/stratigraphic "control" surfaces used to constrain models. Adequate modeling of reservoir heterogeneity appears to require very to extremely large models (e.g. large number of small cells). Many of the parameters used to "control" heterogeneity including the semivariogram range parameter, the number of "detailed" stratigraphic layers, and the number of rock/facies "containers" or model regions appears to have only a small impact on forecast recovery.