Se, Yegor (Chevron Energy Technology Company) | Villegas, Mauricio (Chevron) | Iskakov, Elrad (Chevron) | Playton, Ted (Tengizchevroil) | Lindsell, Karl (Tengizchevroil) | Cordova, Ernesto (Chevron Energy Technology Company) | Turmanbekova, Aizhan | Wang, Haijing
Secondary oil recovery projects in naturally fractured carbonate reservoirs (NFR) often introduce uncertainties and challenges that are not common to conventional waterfloods. The recovery mechanism in NFRs relies on ability of the fracture network to deliver enough injected fluid to the matrix, as well as rate and magnitude of capillary interactions within the matrix rock, during which hydrocarbon displacement occurs. The imbibition measurements can be performed in the laboratory using core samples, but due to reservoir heterogeneity, certain limitations of the lab equipment and the quality of the core material, scalability of the core results to a reservoir model can be challenging.
This paper describes the design, execution and evaluation of the’ log-soak-log’ (LSL) pilot test conducted in a giant naturally fractured carbonate reservoir with a low-permeability matrix in Western Kazakhstan, where repeatable and reliable measurements of changes in water saturation were achieved across large intervals (tens of meters) using a time-lapse pulsed-neutron logging technique. Periodic measurements provided valuable observations of dynamic change in saturation and fluid level over time and allowed estimation of the rate and magnitude of imbibition in the slope margins, depositional settings and rock types of interest. Incorporation of the LSL results into reservoir models validated the ranges of water-oil relative permeability curves, residual oil saturation to water, irreducible water saturation, and capillary pressure assumptions. This validation constrained key subsurface uncertainty and updated the oil recovery forecast in several improved oil recovery (IOR) waterflood projects.
Se, Yegor (Chevron U.S.A. Inc) | Galimzhanov, Saken (Tengizchevroil) | Amangaliyev, Bolat (Tengizchevroil) | Aitzhanov, Abzal (Tengizchevroil) | Yechshanov, Ilyas (Tengizchevroil) | Iskakov, Elrad (Chevron U.S.A. Inc) | Ghomian, Yousef (Tengizchevroil) | Bopiyev, Chingiz (Tengizchevroil) | Wang, Haijing (Chevron U.S.A. Inc)
Sour gas injection (SGI) in the non-fracture platform area of the giant carbonate oil field, Tengiz, began in 2007. SGI project was proven to successfully maintain reservoir pressure in the platform area, add significant reserves, reduce sulfur production, and enable additional oil processing capacity at the crude processing facility. Despite the confirmed benefits, the gas breakthrough and increasing gas-oil ratio (GOR) trends in several SGI producers became a concern as the injection project matured. The preferential production from wells with lower GOR allowed higher total oil throughput, but also introduced production constrain on SGI wells with higher GOR. As the result, SGI producers were historically choked back or completely shut-in as soon as the gas breakthrough was confirmed and the producing GOR began to increase above 500m3/m3.
The reservoir heterogeneity with the sour gas injection overprint created complex dynamic environment at the subsurface. Special surveillance program was designed to improve understanding of gas front movement through the reservoir, assess vertical and areal sweep efficiency and remaining oil in place in various zones of interest. Surveillance program design had to overcome several operational constrains, such as wellbore accessibility issues from the scale build, gas handling limits of the surface facilities, and complex simultaneous operations near the active high-pressure sour gas compressor. Moreover, the log interpretation had to consider crossflow and stimulation chemicals impact on the logging measurements. Finally, the integration of logging interpretation results with reservoir model was required to improve the reservoir model forecast and boost the value of acquired information.
This paper describes the results of the conducted surveillance campaign, the novel calibration methodology of gas saturation profile from the time-lapse cased hole measurements with proxy from the multi-component simulation model output and the early results of the performed gas shut-off operations. The described methodology allowed direct calibration of the model outputs with the gas saturation results from pulse neutron logs and provided more accurate sweep efficiency and oil recovery forecast across the entire SGI area. Calibrated model revealed consistent gas breakthrough profile and significant volume of low GOR oil remaining in the wells with gas breakthrough.
The updated reservoir model was then used to evaluate various development scenarios of SGI area. Gas shut-off scenario showed particularly encouraging low GOR production trends and improved oil recovery especially from the lower intervals. After the economic analysis, several wells, including long-term shut-ins, were added to the workover queue to timely realize production benefits. Early production results after gas shut-off workover consistently met or exceeded model forecasts. Described methodology provided more accurate scope definition, value assessment and justification for the SGI optimization project and could be applicable to other improved oil recovery projects.
In the absence of well-developed calibrated geologic and simulation models, empirical approaches such as decline curve analysis (DCA) are normally used for production forecasting and reserve estimation. DCA is computationally more efficient compared to simulation models when the active well base exceeds hundreds of wells. However, the underlying assumption for conventional DCA is no change in well operation settings. Moreover, the common approach for production forecasting consists of manual outlier detection and removal, interpretation of missing measurements and data fitting using different models for each well. Therefore, the process of conventional DCA is subjective due to the lack of a standard workflow for preprocessing and data cleansing. The common practice for doing DCA has three main steps: 1. Finding the most representative period in the history of well, 2. Detecting the initial rate (start point) of forecast, 3. Selecting the type of decline and fitting the appropriate model to data points. The solutions to these problems could vary from engineer to engineer and it can be time consuming to analyze all wells manually. To address these issues, we developed a novel workflow based on stochastic methods for detecting various well interventions including change in artificial lift, pump changes and acid treatment, and for forecasting oil production rate more accurately in the presence of uncertainty. The novelty of the proposed ensemble-based approach is forecasting conditioned on various well interventions. Furthermore, the proposed unsupervised stochastic anomaly detection method will detect various well works (or events) in the case of missing records of time and type of events. In this paper, we designed two experiments to test the proposed workflow for oil production rate forecasting and evaluation of acid treatments.
A. H. Khan, M. Faisal (Pakistan Petroleum Limited) | Abid, M. Faraz (Pakistan Petroleum Limited) | Fareed, Abdul (Pakistan Petroleum Limited) | Javed, Zeeshan (Pakistan Petroleum Limited) | Khan, M Noman (Pakistan Petroleum Limited) | Hashmi, Shariq (Pakistan Petroleum Limited)
Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries.
Well location map for the studied field
Well location map for the studied field
The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model.
Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place.
Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable "m" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).
Reliability of subsurface assessment for different field development scenarios depends on how effective the uncertainty in production forecast is quantified. Currently there is a body of work in the literature on different methods to quantify the uncertainty in production forecast. The objective of this paper is to revisit and compare these probabilistic uncertainty quantification techniques through their applications to assisted history matching of a deep-water offshore waterflood field. The paper will address the benefits, limitations, and the best criteria for applicability of each technique.
Three probabilistic history matching techniques commonly practiced in the industry are discussed. These are Design-of-Experiment (DoE) with rejection sampling from proxy, Ensemble Smoother (ES) and Genetic Algorithm (GA). The model used for this study is an offshore waterflood field in Gulf-of-Mexico. Posterior distributions of global subsurface uncertainties (e.g. regional pore volume and oil-water contact) were estimated using each technique conditioned to the injection and production data.
The three probabilistic history matching techniques were applied to a deep-water field with 13 years of production history. The first 8 years of production data was used for the history matching and estimate of the posterior distribution of uncertainty in geologic parameters. While the convergence behavior and shape of the posterior distributions were different, consistent posterior means were obtained from Bayesian workflows such as DoE or ES. In contrast, the application of GA showed differences in posterior distribution of geological uncertainty parameters, especially those that had small sensitivity to the production data. We then conducted production forecast by including infill wells and evaluated the production performance using sample means of posterior geologic uncertainty parameters. The robustness of the solution was examined by performing history matching multiple times using different initial sample points (e.g. random seed). This confirmed that heuristic optimization techniques such as GA were unstable since parameter setup for the optimizer had a large impact on uncertainty characterization and production performance.
This study shows the guideline to obtain the stable solution from the history matching techniques used for different conditions such as number of simulation model realizations and uncertainty parameters, and number of datapoints (e.g. maturity of the reservoir development). These guidelines will greatly help the decision-making process in selection of best development options.
This paper presents the background, implementation, and initial results of a pilot project to address the shortage of qualified petroleum engineers in developing countries. Oil and gas talent gap in emerging markets was identified as an eminent problem by the Steering Committee of the World Economic Forum's (WEF) Oil & Gas Community in 2017. Chevron, Eni, and Shell acted on the initiative of WEF and, with the addition of Colorado School of Mines (Mines) as the academic partner, kicked off a pilot project to improve the Petroleum Engineering (PE) program at Satbayev University (SU) in Almaty, Kazakhstan, in 2018. The WEF working group, consisting of the representatives of the three companies and the department heads of Mines and SU, identified three priority areas: (1) Establishment of an Industry-Advisory Board (IAB) to promote mutual trust and collaboration between academia and industry, (2) Curriculum revision and improvement of the course material and delivery with the support of Mines, and (3) Student and faculty internship programs to provide industry training and support for faculty development. Many challenges of the Kazakh PE education are common to the other emerging oil and gas producing countries also. Therefore, the lessons learned from this project will be useful to develop similar projects not only in Kazakhstan but also around the world. This paper presents the details of implementation, challenges encountered, and initial results of the project.
Kazakhstan, once one of the 15 republics of the Soviet Union, has gained international prominence in the oil and gas scene. The country is a rising star in the oil and gas industry, with rich hydrocarbon reserves and several world-scale projects such as the Tengiz and Karachaganak field developments. With a burgeoning oil and gas industry, petroleum engineering and geology students are becoming more active in seeking out development and networking opportunities. Technical U. (KNTU) organized its third international youth forum on "Future Caspian Oil and Gas" in Almaty, Kazakhstan, during 15–16 April. Approximately 300 students attended from universities across Kazakhstan, Russia, Uzbekistan, and Kyrgyzstan.
Albertini, Cristian (Eni Spa) | Bigoni, Francesco (Eni Spa) | Francesconi, Arrigo (Eni Spa) | Lazzeri, Riccardo (Eni Spa) | Vercellino, Alberto (Eni Spa) | Borromeo, Ornella (Eni Spa) | Gabellone, Tatyana (Eni Spa) | Consonni, Alberto (Eni Spa) | Geloni, Claudio (Eni Spa)
The reservoir quality of Karachaganak Carbonates Field results significantly affected by diagenetic processes. In particular, the replacive dolomitization affects porosity, permeability and irreducible water saturation while the precipitation of anhydrite reduces both porosity and permeability. Such impacting processes were therefore analysed and described in the reservoir 3D Model following geologically consistent rules that honour well data.
The field scale diagenetic study was performed following five steps:
Core data studies Lithological logs analysis Hydrological processes identification Hydrological processes reactive transport simulations 3D Lithological model building
Core data studies
Lithological logs analysis
Hydrological processes identification
Hydrological processes reactive transport simulations
3D Lithological model building
The dolomite distribution, estimated from the lithological log analysis and cores data, results mainly confined on the flanks of the paleo-high. This distribution was endorsed by the results of 3D field scale reactive transport modelling related to Kohout geothermal convection mechanism acting in the shallow burial of the carbonate paleo-high at each stratigraphic unit. The final lithological 3D Model was built consistently with this hydrological process calibrated with well data used as verification data set in the stochastic simulations.
The anhydrite distribution, estimated from lithological log analysis and cores data, is, generally, present in a few percentage of volume and, mainly, in the upper section of the reservoir (less than 250 m, below the bottom of the overlaying Kungurian evaporites). This anhydrite was related to diffuse downward percolation of the Kungurian brine and, marginally, to dolomitization. The occurrence of higher concentration of anhydrite was also locally observed but generally connected to fracture infill and, sometimes, also in the deeper section of the reservoir. These events were related to brine percolation exploiting a network of syn-depositional fractures, particularly along the flanks of the carbonate bank (Neptunian dykes). Such hydrological processes was endorsed by 2D reactive transport modelling. In fact, the anhydrite infilling fractures may have a significant impact on the reservoir flow path and therefore a workflow for identification of these Neptunian dykes was applied, based on seismic attributes (Continuity and Curvatures) according to the Eni proprietary workflow utilized for the identification of sub-seismic discontinuities (Tfrac-Sibilla).
The so estimated dolomite distribution represents about the 15% of the lithology at field scale but up to the 60% on the flanks of the carbonate build-up, marginal areas investigated by very few wells but impacting on about the 30% of the field total GBV. Accordingly, the petrophysical characteristics of the field flanks result affected, in the 3D Reservoir Model, by the presence of dolomite, i.e. increased porosity, permeability and irreducible water saturation. Moreover, the identification of the sub-seismic discontinuities filled by anhydrite allows a better description of the permeability baffles affecting the 3D model flow paths.
Saputelli, Luigi (ADNOC) | Celma, Rafael (ADNOC) | Boyd, Douglas (ADNOC) | Shebl, Hesham (ADNOC) | Gomes, Jorge (ADNOC) | Bahrini, Fahmi (Frontender Corporation) | Escorcia, Alvaro (Frontender Corporation) | Pandey, Yogendra (Prabuddha)
Permeability and rock typing are two of the main outputs generated from the petrophysical domain and are particularly contributors to the highest degree of uncertainty during the history matching process in reservoir modeling, with the subsequent high impact in field development decisions. Detailed core analysis is the preferred main source of information to estimate permeability and to assign rock types; however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to predict permeability and rock types in each data point of the reservoir model.
The approach of this investigation is to apply data analytics and machine learning to move from the core domain to the log domain and to determine relationships to then generate properties for the three-dimensional reservoir model with proper simulation for history matching. All wells have a full set of logs (Gamma Ray, Resistivity, Density and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. Then, core data is used to define petrophysical groups (PG), followed by linking the PG's to NMR pore-size distribution analysis results into pre-determined standard pore geometry groups, in this step supervised PGs are generated from the log response constrained by the relationship between pore-throat geometry (MICP) and pose-size distribution (NMR). Permeability-porosity core relationships are reviewed by sorting and eliminating the outliers or inconsistent samples (damaged or chipped, fractures or with local features). After that, the supervised PGs are used to train and calibrate a supervised neural network (NN) and permeability and rock type's relationships can be captured at log scale. Using dimensionality reduction improves the neural network relationships and thus data population into the petrophysical wells.
The result is a more robust model capable to capture over 80% of the core relationships and able to predict permeability and rock types while preserving the geological features of the reservoir. The application of this method makes possible to determine the relevance of core and log data sources to address rock typing and permeability prediction uncertainties. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.
This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.