Raghunathan, Murali (ADNOC - Al Dhafra Petroleum Company) | Alkhatib, Mohamad (ADNOC - Al Dhafra Petroleum Company) | Al Ali, Abdulla Ali (ADNOC - Al Dhafra Petroleum Company) | Mukhtar, Muhammad (ADNOC - Al Dhafra Petroleum Company) | Doucette, Neil (ADNOC - Al Dhafra Petroleum Company)
A novel workflow was developed to select an optimal field development plan (FDP) which accounts for a number of associated uncertainties for an oil Greenfield concession that has a limited number of wells, production data and information. The FDP was revisited and updated to address the additional data acquired during the field delineation phase. The study in Ref-1 demonstrates the comprehensive uncertainty analysis performed and the resulting optimized FDP. The FDP was developed to minimize the economic risk and uncertainty. Further field delineation activities have revealed a north and south extensions with an increase in hydrocarbon accumulation by 115%. A reservoir dynamic model was updated because of the increase in HC and input data from 17 wells. A workflow has been created with a suitable development option to consider the recently appraised areas, which are: - Updated saturation height functions (SHFs) which improve the match between newly drilled wells and water saturations logs - Updated reservoir models which were based on well tests and new analytical interpretations - History matching well test data with new acquisition data - Optimized field development options, that cover additional areas - Inputs to reservoir surveillance plan Be implementing following an extensive analysis the most robust development concept was selected and will now in the field.
The vast majority of grids for reservoir modeling and simulation workflows are based on pillar gridding or stairstep grid technologies. The grids are part of a feature-rich and well-established modeling workflow provided by many commercial software packages. Undesirable and significant simplifications to the gridding often arise when employing such approaches in structurally complex areas, and this will clearly lead to poor predictions from the downstream modeling.
In the classical gridding and modeling workflow, the grid is built in geological space from input horizon and fault interpretations, and the property modeling occurs in an approximated ‘depositional’ space generated from the geological space grid cells. The unstructured grids that we consider here are based on a very different workflow: a volume-based structural model is first constructed from the fault/horizon input data; a flattening (‘depositional’) mapping deforms the mesh of the structural model under mechanical and geometric constraints; the property modeling occurs in this depositional space on a regular cuboidal grid; after ‘cutting’ this grid by the geological discontinuities, the inverse depositional mapping recovers the final unstructured grid in geological space. A critical part of the depositional transformation is the improved preservation of geodetic distances and the layer-orthogonality of the grid cells.
The final grid is an accurate representation of the input structural model, and therefore the quality checking of the modeling workflow must be focused on the input data and structural model creation. We describe a variety of basic quality checking and structurally-focused tools that should be applied at this stage; these tools aim to ensure the accuracy of the depositional transformation, and consequently ensure both the quality of the generated grid and the consistent representation of the property models. A variety of quality assurance metrics applied to the depositional/geological grid geometries provide spatial measures of the ‘quality’ of the gridding and modeling workflow, and the ultimate validation of the structural quality of the input data.
Two case studies will be used to demonstrate this novel workflow for creating high-quality unstructured grids in structurally complex areas. The improved quality is validated by monitoring downstream impacts on property prediction and reservoir simulation; these improved prediction scenarios are a more accurate basis for history matching approaches.
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.
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.
Field K1 as part of AA Tight Gas Cluster features significant variability in the fluid properties, concluded through PVT, well test as well as geochemical measurements. Following an extensive data acquisition program that was conducted at the beginning of the project, a multi-disciplinary review and integration of data was carried out in order to adequately characterize the fluid distribution across the field.
Several analyses were employed to understand the characterization of the fluid distribution through geochemistry analysis, compositional gradient analysis and lateral fluid investigation.
Gas samples and mud gases were collected during drilling and analyzed for gross composition and stable carbon isotope for geochemical analysis purposes. Condensates were collected and analyzed for gross composition, sulfur content and isotopic analyses. Analyses of both fluid types aimed at gathering reliable information in terms of source type and thermal maturity of gases. The large number of data points from high resolution sampling of mud gases allowed for a more confident examination of charge history and communication of the Upper Amin Formation across the cluster of fields.
The gas and condensate samples were taken after well completion for further PVT analysis. Gas composition, temperature, fraction of liquid drop-out and measured Dew Points suggested complex reservoir fluid and genetically different behavior with a contrasted fluid signature across Field K1. Plotting the fluid composition, phase envelopes, as well as Dew Point gradient supported application of a complex-fluid modeling together with segmentation.
The understanding of the fluid behavior is important for the reservoir description as well as the overall development plan of Field K1. The impact on the development plan includes: missing condensate recovery opportunity, on-plot and off-plot facility design, overall gas and condensate recovery factor per well, and the sequence of development.
This new analysis resulted in an upward update in resource volume estimation of Field K1. Well placement and drilling sequence optimization were derived as the positive outcome of this exercise.
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.
Li, Xin (Zhenhua Oil Company Research Center) | Huang, Haiping (Zhenhua Oil Company Research Center) | Lu, Hao (State Key Laboratory of Oil and Gas Reservoir Geology and Exploration, Southwest Petroleum University)
Knowledge of permeability is critical for developing an effective reservoir description. The accuracy of petrophysical models of permeability from conventional methods is relatively low. In this paper, the concept of flow units is used to evaluate formation permeability for a carbonate reservoir located in Iraq. Based on comprehensive analysis of core and well log data, flow units are established and subsequently are used to model permeability among several flow units. Correlation between porosity and permeability in each flow unit improves significantly, resulting in the establishment of a permeability model for each unit. Comparison of calculated permeability and core results indicates a highly accurate permeability model. Mercury injection data including Pc10, Rc50 and pore throat size, integrated with thin section observations, are also used to evaluate the physical properties of three distinct flow units. Permeability profiles are generated for each flow unit using well log data agree with core data. This agreement illustrates the potential and applicability of the flow unit method in complicated carbonate reservoirs.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 209A (Anaheim Convention Center)
Presentation Type: Oral
Yang, Xiangtong (PetroChina) | Qiu, Kaibin (Schlumberger) | Zhang, Yang (PetroChina) | Huang, Yongjie (Schlumberger) | Fan, Wentong (PetroChina) | Pan, Yuanwei (Schlumberger) | Xu, Guowei (PetroChina) | Xian, ChengGang (Schlumberger)
Keshen is a high-pressure/high-temperature (HP/HT) tight-sandstone gas reservoir with reservoir pressure greater than 110 MPa and temperature more than 175°C. The sandstone is hard, with unconfined compressive strength (UCS) greater than 100 MPa. Given the HP/HT nature and natural-fracture systems in the reservoir, with aid of stimulation, many wells produced at a high rate, with the mean value exceeding 500 000 m3/d. In the last few years, many production wells in this reservoir experienced severe sanding issues that contradicted the conventional understanding that sanding would not occur in such hard rock. The sanding wells exhibited large fluctuations of production rate and wellhead pressure, erosion of chokes and nozzles, and eventually major or even complete loss of production. A solution to address the sanding issues was urgently needed because they had caused a major decline in production and resulted in significant economic loss.
Because of the unconventional nature of the sanding issues, the typical sanding-prediction methods dependent on evaluating rock failure were not adequate to reveal the underlying sanding mechanism and develop a viable operational solution. To this end, a new work flow was formulated and applied to this study. The work flow started with detailed data mining on the large amount of drilling, completion, stimulation, and production data of more than 51 wells from this reservoir to investigate possible relationships of drilling practices, completion options, and production schedules to the occurrence and severity of sanding issues. The analysis revealed that downhole flow velocity and production drawdown were the two major controlling factors in the occurrence of sand production. Further geomechanics simulation and particle-migration simulation with a multiphase dynamic flow simulator confirmed that the production drawdown would cause failure of the rock near the wellbore and the gas flow could transport the sand debris to the wellbore and lift it up to the surface. In addition, the fluctuation of production rate was caused by blockage because of the accumulation of sand particles in the wells and production tubing that were flushed out after downhole-pressure buildup.
Using the analysis, the threshold of flow velocity and the threshold of drawdown were identified, and these thresholds can be used in the reservoir management to effectively address the sanding issues.
The experience in Keshen shows that sanding is possible in HP/HT high-productivity sandstone gas reservoirs, even in an extremely hard formation, which overturns some prior conceptions on sanding. The information shared from this paper could attract the attention of those operating similar HP/HT tight-sandstone reservoirs around the world.
Liang, Baosheng (Chevron North America Exploration and Production) | Khan, Shahzad A (Chevron North America Exploration and Production) | Puspita, Sinchia Dewi (Chevron North America Exploration and Production)
It is important to determine several key parameters, such as well spacing, completions design, landing strategy, and pad sequence, for a successful full-field development of the unconventional reservoir that involves multiple wells and pads in a given area of interest. Those parameters are normally considered individually through small and simple models. In this paper, focusing on developing the whole area effectively, we provided a systematic work flow to handle such challenges together: We first recommended a top-down concept that better represents actual field development and illustrates the importance of the 3D Earth model for the unconventional reservoir; we then proposed an integrated modeling that is an iterative loop consisting of the 3D Earth model, hydraulic-fracture modeling, reservoir simulation, and uncertainty analysis.
It is uncommon to build a 3D Earth model for the unconventional reservoir mainly because of the lack of data and software capability. In this paper, we provided a cost-effective approach for the first time on the basis of a large amount of existing vertical wells, newly drilled horizontal wells, and all the data available. A 3D Earth model using information from approximately 1,100 vertical wells from the Midland Basin was presented. Such a model has a high resolution conditioned by high well density, and has an advantage of capturing hetrogeneities and interactions more than a simplified model created either from one well or low-resolution seismic interpretation. The model was fed into hydraulic-fracture modeling with the consideration of natural-fracture network and stress shadow, followed by reservoir simulation. The in-house uncertainty-analysis package that functions by experimental-design philosophy is linked to the Earth model, hydraulic-fracture modeling, and reservoir simulation. For the first time, the impacts of all the parameters together were evaluated through the final production performance. In our example, we considered completions design, discrete-fracture-network (DFN) characterization and generation, unpropped hydraulic-fracture properties, fracture compaction, and matrix permeability. The result indicated that DFN characterization is the most important parameter affecting production performance.
We applied our model and work flow to field development. Well spacing and pad sequence were studied in this paper as two examples. We demonstrated that it is important to properly consider complex interactions among multiple clusters, stages, and wells to evaluate the impacts on well spacing, completions, and development sequence.