A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.
Full field development of the Upper Jurassic carbonates, offshore Abu Dhabi is exceedingly challenging. The heterogeneous texture, complicated pore systems and intensive lithology changes all mark the regressive cycles of sedimentation. Such complicated characteristics obscure formation evaluation of these formations. Advanced well logging tools and interpretation methodologies are implemented to minimize the petrophysical uncertainties to qualify the products as field development critical elements. This case study highlights a newly applied NMR log interpretation approach. The results help to understand the complex pore system in a tight carbonate layer, along a horizontal drain drilled close to the oil-water contact.
NMR log data was acquired in real-time while drilling simultaneously with Gamma Ray, Resistivity and Image Logs. Earlier field studies recommended swapping standard T2 free fluid relaxation cutoff values by actual laboratory NMR measurements for a higher precision suitable for the reservoir texture heterogeneity, the study itself supported the application of higher cutoff values to better discriminate the free fluid in well-connected macro pores from the irreducible which will have a direct impact on the computed permeability.
In this case study, a variable free-fluid T2 cutoff was firstly implemented based on arbitrary estimations to match the computed Coates permeability to the offset core values. Free-fluid, irreducible fluids were sequentially computed. A unique NMR-Gamma Inversion (NMR-GI) workflow is further utilized as a mathematically defined approach to process the raw data using probabilistic functions. The result is a more precise pore size distribution, coherent with the geological variations. NMR Capillary pressure was computed.
The complex formation texture could be accurately tracked for thousands of feet drilled along the horizontal drain. After validation with offset core, the NMR-GI interpretation was combined with, Archie saturation and Image log analysis for a conclusive assessment. Hydraulic flow units were combined. Successful completion design and production zone selection articulated on the defined open hole log interpretation.
NMR while drilling logging and the applied (NMR-GI) methodology prove to be leading tools to assist in resolving carbonate reservoir complexities. Not only that they help to understand the pore system characteristics, but they effectively support well placement, completion and production.
The last session is going to be a forward-looking discussion influenced from the key takeaways of the whole week. One last time we will have a common debate aiming for expanding the utilization of deep reading while revising the barriers we must overcome. Unresolved topics and gaps will be identified for providing recommendations to research and development. The closing session may also recognize topics that needs to be discussed in future SPE Forums.
To estimate Rt under a variety of different logging conditions and in different formations, a simple three-parameter, step-profile invasion model is often used. This model consists of a flushed zone of resistivity Rxo and a sharp boundary at diameter di, with the uninvaded zone of resistivity Rt. Three independent, borehole-corrected resistivity measurements with appropriately chosen depths of investigation contain enough information from the formation to reliably solve for Rt using this model. Measurements with the following features should be chosen: small, correctable borehole effects; similar vertical resolutions; and well-distributed radial depths of investigation--one reading as deep as practical, one very shallow reading, and one intermediate reading. In conductive muds, the Dual Laterolog (DLL) Resistivity– Rxo combination tool provides simultaneous measurements suitable for evaluating Rt, Rxo, and di. It should be said that the value of Rt in a given bed is an interpreted parameter, and is almost never measured.
In some instances, the uncertainty can be significant, and additional information is needed to optimize production and improve estimates of ultimate recovery. In many cases, the effect of the changing reservoir pressure and/or saturation on seismic data can be used to map the changing pattern of these reservoir properties by obtaining seismic data repeatedly during production of the reservoir. With care, seismic data obtained for other purposes (such as regional exploration) can sometimes be used for time-lapse seismic monitoring, but new data are often obtained from seismic experiments designed particularly to monitor the reservoir. The desire to minimize differences in acquisition parameters between surveys has led, in some cases, to permanent installation of sensors in the oilfield. Because most sensors deployed in this manner are deeply buried and/or cemented, this also has the effect of removing many of the sources of random seismic noise.
The ability of seismic reflection technology to image subsurface targets is possible largely through the geometry of sources and receivers. A method similar to triangulation is used to place reflections in their correct locations with (more-or-less) correct amplitudes, which can then be interpreted. The amplitudes are indicative of relative changes in impedance, and the seismic volume can be processed to yield impedances between the reflecting boundaries. The geometry of sources and receivers in a typical reflection seismic survey yields a number of seismic traces with common midpoints or central bins for stacking. These traces were recorded at different offset distances, and the travel times for seismic waves traveling to and from a given reflecting horizon varies with that distance (Figure 1).
Integral transforms are useful in solving differential equations. A special form of the linear integral transforms, known as the Laplace transformation, is particularly useful in the solution of the diffusion equation in transient flow. The following fundamental properties of the Laplace transformation are useful in the solution of common transient flow problems. For the Laplace transform to be useful, the inverse Laplace transformation must be uniquely defined. In this operation, p(t) represents the inverse (transform) of the Laplace domain function, .
Resistivity is the one of the most difficult formation parameters to measure accurately because of the complex changes that occur during and after drilling a well and that may still be occurring during logging. The various components of the downhole environment may have strongly contrasting resistivities, some of which cannot be measured directly, and their physical dimensions may not be readily available. Figure 1 shows an idealized relationship of the main environmental components. There is no direct measurement of Rt. It must be inferred from the multiple-depth resistivity measurements.
Almost simultaneously, advances were made in understanding both the processes within the source rock organic matter that accompany the generation and expulsion of hydrocarbons and in the acquisition, processing, and quantitative interpretation of 3D seismic data. In particular, as organic matter in shales in unconventional plays generates and expels hydrocarbons, porosity is formed in the organic matter and the organic matter becomes more dense and more brittle. As these changes are occurring at a micro-scale, extraction of hundreds of different attributes from a well-imaged 3D seismic volume has made it possible to observe changes at a macro-scale in seismic lines and horizons within that volume. Seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to calculate TOC, pore pressure, rigidity, and compressibility because these properties cause fundamental changes in how seismic waves travel through the rock.
Equally important, the escalation in computing power via methods such as machine learning, neural networks, and multivariate statistics has made it possible to interpret large amounts of data. All of these innovations have contributed to better identification of sweet spots within unconventional plays. Such sweet spots include areas with elevated TOC values, enhanced porosity, and zones that can be targeted for fracking.
One of the primary advantages of seismic data is that it provides information in those areas in between control points/wells. This information in turn helps operators to better select targets for wells and for landing zones. Carefully tied 3D seismic inversion and integration with petrophysical and rock data further allow for detailed characterization of unconventional reservoirs. The enhanced ability to identify the best potential drilling targets has significant economic implications in terms of risk reduction and improved chances to find economic prospects.
While 3D seismic data is being used routinely by numerous companies to predict the mechanical properties, density, and associated TOC of many formations, there is yet to be a direct link made between TOC loss, kerogen conversion, and the associated changes in rock properties. This work documents the importance of TOC loss during maturation and its effects on rock properties like porosity, density, brittleness, and how those advances coupled with the advances in quantitative interpretation of 3D seismic data are enabling the unconventional operators to predict location, thickness, landing zone, and sweet spots with appropriately acquired, processed, and interpreted 3D seismic. Meticulously calibrated 3D seismic inversion and integration with petrophysical and rock data permit detailed reservoir characterization of unconventional reservoirs.
Updated methods for the back calculation of original TOC have been developed using well logs, rock measurements, and 3D basin modeling to assist in locating and developing unconventional reservoirs. In addition, petrophysical measurements that reflect TOC and porosity and are related to fundamental properties controlling the seismic response can be extracted from the seismic reflection data. In turn, seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to estimate TOC, pore pressure, rigidity, and compressibility because these properties cause basic modifications in how seismic waves travel through the rock.
This study shows advancements in studies of: 1) TOC loss with increased thermal maturation, 2) how this loss affects the development of organic porosity, 3) how kerogen becomes denser, harder, and more brittle with increasing maturity, and 4) how recent developments in quantitative interpretation workflows for 3D seismic data facilitate estimation of TOC and determination of rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density. Further integration of geochemical, geomechanical, and geophysical technologies and measurements will provide improved estimates of present-day TOC that can in turn be extended to relative maturity and percent conversion.
Examples provided in this work illustrate prediction of present-day TOC, porosity, density, and mechanical properties extracted from high fidelity pre-stack inversion. Pre-stack inversion along with machine learning can be used to predict rock properties such as porosity, TOC, organic matter quality, rigidity, and pressure and to correlate those properties back to well productivity for improved execution. Relating present TOC estimated from seismic to TOC loss and kerogen property changes with increasing maturity is possible by combining the results of these technologies.
Though analysis and inversion of painstakingly acquired modern 3D seismic data is capable of estimating porosity, TOC, matrix strength, and pore pressure, the latest work on rock property changes as hydrocarbons mature and are expelled isn't typically addressed in most studies. Increasing communication between disciplines might improve estimation of these properties and extend the capability to assess the extent of TOC loss during maturation and the porosity increases that accompany it. This ability is especially important in the intra-well regions where the potential of 3D seismic to extend data between control points enables better reserve estimates and high grading of acreage. After carefully calibrating a quantitative 3D seismic interpretation with a 3D basin modeling analysis of the source rock potential and maturity, an operator is better prepared to high grade acreage and attain the most economic development of unconventional resources.
The escalation in computing power means there are hundreds of different attributes that can be extracted or calculated from a well-imaged 3D seismic volume. Using quantitative calibration of fundamental geochemical measurements such as TOC, pyrolysis, and petrographic measurements of vitrinite reflectance that yield the quantity, quality, and maturity of organic matter in combination with well log and seismic data creates a model for identifying sweet spots and the areas in the target formation that exhibit high TOC, high porosity, and elevated brittleness. Further integration and calibration of changes occurring at the micro-level in organic matter in unconventional plays with their impact on the signatures of data at the macro-level can provide information on the types of hydrocarbons most likely to be found in these sweet spots as well as identifying which zone(s) in the target formation are most likely to be amenable to fracking. Used together, the advances outlined here result in a technological evolution that could have a substantial impact on: 1) the approach to and 2) the economics of the exploration and production of unconventional plays.
Pore pressure prediction plays a critical role in the ability to predict areas of high overpressure and fracture behavior for the exploitation of unconventional plays, which are both correlated with production. Shales in these plays have variable clay content and complex multi-mineral fractions that require a detailed petrophysical assessment reinforced with rock physics modelling as needed. For example, changes in total organic content have a similar elastic response to changes in porosity. Therefore, any pressure-stress property model for unconventional plays must be supported by petrophysically conditioned elastic logs and accurate multi-mineral volume sets calibrated to core data.
A supervised deep neural network approach is introduced as an alternative innovative tool for petrophysical, pore pressure and geomechanics analysis enabling the use of all the previously collected and interpreted data to devise solutions which simultaneously integrate wide ranging well bore and wireline logs. We implement three neural networks, all with similar structure, as each of these networks had a different objective and the outputs from one were the inputs for the other.
The first network was trained to predict petrophysical volume logs (shale, sand, dolomite, calcite, kerogen and also porosity) simultaneously from compressional velocity (Vp), Gamma ray, density (rho), resistivity and Neutron logs. The second neural network, cascaded from the first, was then designed to match the manually predicted pore pressure. The inputs were Vp and shear velocity (Vs), Rho, resistivity, Neutron logs as well as the results of the first network. The third network focused on predicting various properties of interest, in this case pore pressure, minimum horizontal stress (Shmin), maximum horizontal stress (SHmax), and volume of kerogen, based on only Vp, Vs, and Rho logs which is an example building a neural network capable of predicting key rock properties directly from seismic inversion results to produce meaningful 3D interpretations.
The volumetric pore pressure model was also positively correlated to cumulative production values from blind long horizontal wells. The results show a promising outlook for the application of deep learning in integrated studies such as those shown in this paper.