Altering Natural Gas Composition In-situ During Production

Abstract

Methods and systems for producing gas from a subsurface formation through a production well and / or sequestering gas in the subsurface formation through an injection well can include monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well. The pressure, the temperature, and the composition of fluids in the subsurface formation can be monitored using sensors installed downhole in the production well(s) and / or observation well(s). This approach can predict a volume of gas injection through required to alter the composition of fluids in the subsurface formation to provide a specific fluid composition in the production well. It can also include injecting the predicted volume of gas through the injection well using pumps associated with the injection well as well as, in some cases, producing the fluids in the subsurface formation to surface.

Description

TECHNICAL FIELD

[0001] This specification generally relates to producing natural gas from subsurface formations, particularly using both injection and production wells.BACKGROUND

[0002] Enhanced gas recovery can enable production of natural gas from formations where it would otherwise not be economical. Such formations can include shale and tight gas systems. Enhanced gas recovery techniques include hydraulic, pneumatic, and thermal fracturing, carbon dioxide (CO2) injection, mechanical cutting of shale formations, and enhanced bacterial methanogenesis. In some situations, injecting CO2 into a formation can enhance gas recovery while also providing geologic storage of the CO2.SUMMARY

[0003] This specification describes an approach to producing natural gas from subsurface formations while altering gas composition in-situ during production. This approach uses a system that predicts the required gas injection volume to alter the subsurface gas composition. By altering gas composition in-situ during production, this approach helps produce a defined gas composition from the wells and the field, even when dealing with, for example, natural gas containing high hydrogen sulfide (H2S) concentrations.

[0004] In some implementation, this system uses real-time pressure, volume, temperature (PVT) from downhole samples and well logs with a machine learning model to predict injection volumes that will produce a high quality gas mix on surface. An example implementation of this approach can start by adding a production well and an associated injection well to a field. The production and injection wells can be equipped with sensors (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) to monitor the current downhole conditions downhole for the training of the machine learning model. The gas chromatograph analyzes the gas sample to determine its composition, while the pressure gauge and temperature gauge provide the pressure and temperature data to be used for PVT analysis. The fluid density meter is used to determine the density of the injected fluid.

[0005] The approach disclosed in this specification can provide one or more of the following advantages.

[0006] Gas brown fields (i.e., previously produced gas fields) can be challenging to produce due to high production of impurity byproducts (e.g., H2S and CO2). Some fields remained undeveloped due to their high capital and operating costs. The major cost drivers for such fields are the high capital of anti-corrosive facilities, operating cost to maintain the facilities safely integrated, and / or costs related to safeguarding the environment and reducing CO2 emissions. This approach can provide an assessment of the impacts of the injection parameters, sweep patterns and compositional changes in subsurface to improve, for example, carbon sequestration and enhanced gas recovery (CSEGR) that is not limited by the experimental control and accuracy. In particular, this approach can assess the compositional changes in real time while injecting / producing using a machine learning model that is fed with the monitoring data and used to make predictions to improve the injection / production parameters and reach the desired gas composition produced.

[0007] This approach has the potential to reduce the capital investment required to develop brown fields and other reservoirs with high H2S concentrations. It can monitor subsurface compositional changes and injection gas sweep in real time, reduce production of H2S or other undesired components, and enhance injection / production cycles. For example, H2S is toxic and corrosive in nature posing significant risk to human health, safety, and environment (e.g. assets damaged by corrosion / leakage or personnel injured or death). By injecting gas (e.g., methane or CO2) into a formation to adjust the composition of gas produced from the formation, this approach can reduce or eliminate the need for measures to deal with H2S at the surface (e.g., H2S detection, H2S monitoring, H2S removal and treatment, and anti-corrosion metals / pipelines). These measures increase as H2S concentrations climb above 10% and are particularly expensive as H2S concentrations climb above 25% as the cost of assets, pipes, infrastructure increases drastically. Monitoring PVT parameters in real time from both production and injection wells enables a machine learning model to make predictions based on real time changes in PVT in contrast to simulation model-based approaches that depend on historical data updates. This approach is also much more computationally efficient than simulation model-based approaches.

[0008] The details of one or more embodiments of these systems and methods are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these systems and methods will be apparent from the description and drawings, and from the claims.DESCRIPTION OF DRAWINGS

[0009] FIG. 1 is a schematic view illustrating hydrocarbon exploration and production activities is a subsurface formation.

[0010] FIG. 2 is a schematic illustrating an example system for producing natural gas from subsurface formations while altering gas composition in-situ during production.

[0011] FIG. 3 illustrates wells being used to producing natural gas from subsurface formations while altering gas composition in-situ during production.

[0012] FIG. 4 is a flow chart illustrating a method of producing natural gas from subsurface formations while altering gas composition in-situ during production.

[0013] FIG. 5 is a diagram illustrating an example computer system configured to execute a machine learning model.

[0014] FIG. 6 illustrates hydrocarbon production operations that include both one or more field operations and one or more computational operations, which exchange information and control exploration for the production of hydrocarbons.

[0015] FIG. 7 is a block diagram illustrating an example computer system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures according to some implementations of the present disclosure.

[0016] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION

[0017] This specification describes an approach to producing natural gas from subsurface formations while altering gas composition in-situ during production and / or enhancing CO2 sequestration. In some implementations, this approach uses a system that predicts the required gas injection volume to alter the subsurface gas composition. By altering gas composition in-situ during production, this approach helps produce a defined gas composition from the wells and the field, even when dealing with, for example, natural gas containing high hydrogen sulfide (H2S) concentrations. In some implementations, the system uses real-time pressure, volume, temperature (PVT) from downhole samples and well logs with a machine learning model to predict injection volumes that will enhance a CO2 sequestration process. For example, this approach can start by adding an injection well for sequestration and an associated observation well to a field. The observation and injection wells can be equipped with sensors (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) to monitor the current downhole conditions downhole for the training of the machine learning model. The gas chromatograph analyzes the gas sample to determine its composition, while the pressure gauge and temperature gauge provide the pressure and temperature data to be used for PVT analysis. The fluid density meter is used to determine the density of the injected fluid.

[0018] In some implementation, this system uses real-time pressure, volume, temperature (PVT) from downhole samples and well logs with a machine learning model to predict injection volumes that will produce high quality gas mix on surface. An example implementation of this approach can start by adding a production well and an associated injection well to a field. The production and injection wells can be equipped with sensors (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) to monitor the current downhole conditions downhole for the training of the machine learning model. The gas chromatograph analyzes the gas sample to determine its composition, while the pressure gauge and temperature gauge provide the pressure and temperature data to be used for PVT analysis. The fluid density meter is used to determine the density of the injected fluid.

[0019] FIG. 1 is a schematic view illustrating hydrocarbon exploration and production activities in a subsurface formation 100. For example, exploration activities including a seismic survey and well logging are illustrated. Illustrated production activities include the production of natural gas using an injection well 123 and associated production well 125. The injection well 123 and the production well 125 have downhole sensors 127 operable to monitor gas conditions and compositions in the subsurface formation 100. A pump 129 can be to inject fluids (e.g., CO2) into the subsurface formation to enhance the flow of gas to the production well 125. Controlling the timing and volume of these injections can also alter gas composition in-situ during production to help produce a defined gas composition.

[0020] The subsurface formation 100 includes a layer of impermeable cap rocks 102 at the surface. Facies underlying the impermeable cap rocks 102 include layers 104, 106, and 108. A fault line 110 extends across the layer 104 and the layer 106.

[0021] Oil and gas tend to rise through permeable reservoir rock until further upward migration is blocked, for example, by the layer of impermeable cap rock 102. Exploration activities attempt to identify locations where interaction between layers of the subsurface formation 100 are likely to trap oil and gas by limiting this upward migration. For example, FIG. 1 shows an anticline trap 107, where the layer of impermeable cap rock 102 has an upward convex configuration, and a fault trap 109, where the fault line 110 might allow oil and gas to flow in with clay material between the walls traps the petroleum. Other traps include salt domes and stratigraphic traps.

[0022] A seismic source 112 (for example, a seismic vibrator or an explosion) generates seismic waves that propagate in the earth. Although illustrated as a single component in FIG. 1, the source or sources 112 are typically a line or an array of sources 112. The generated seismic waves include seismic body waves 114 that travel into the ground and seismic surface waves 115 travel along the ground surface and diminish as they get further from the surface.

[0023] The seismic body waves 114 reflected boundaries between layers are received by a sensor or sensors 116. Although illustrated as a single component in FIG. 1, the sensor or sensors 116 are typically a line or an array of sensors 116 that generate an output signal in response to received seismic waves including waves reflected by the horizons in the subsurface formation 100. The sensors 116 can be geophone-receivers that produce electrical output signals transmitted as input data, for example, to a computer 118 on a seismic control truck 120. Based on the input data, the computer 118 may generate a seismic data output such as, for example, a seismic two-way response time plot.

[0024] The seismic surface waves 115 travel more slowly than seismic body waves 114. Analysis of the time it takes seismic surface waves 115 to travel from source to sensor can provide information about near surface features.

[0025] A control center 122 can be operatively coupled to the seismic control truck 120 and other data acquisition and wellsite systems. The control center 122 may have computer facilities for receiving, storing, processing, and analyzing data from the seismic control truck 120 and other data acquisition and wellsite systems that provide additional information about the subsurface formation. For example, the control center 122 can receive data from a computer 119 associated with a well logging unit 121.

[0026] Computer systems 124 can be located in or at a different location than the control center 122. Some computer systems are provided with functionality for manipulating and analyzing the data, such as performing seismic interpretation or borehole resistivity image log interpretation to identify geological surfaces in the subsurface formation or performing simulation, planning, and optimization of production operations of the wellsite systems.

[0027] In some embodiments, a wellbore 130 that has been drilled in the subsurface formation 100 is logged in a well logging operation 128. The wellbore 130 extends downhole from a wellhead 132. The wellbore 130 is a vertical wellbore but well logging can also be performed in other wellbores, for example, slanted or horizontal wellbores. In the well logging operation 128, the wellbore 130 penetrates through three layers 102, 104, and 106 of a subsurface formation 100. A control truck 121 lowers a logging tool 134 down the wellbore 130 on a wireline 136.

[0028] The computer systems 124 in the control center 122 can be configured to analyze, model, control, optimize, or perform management tasks of field operations associated with development and production of resources such as oil and gas from the subsurface formation 100. For example, an injection well 123 and a production well 125 extend into layer 104 of the subsurface formation 100. Based on data gathered by the exploratory field operations, the computer systems 124 can generate models such as a reservoir model for portions of the subsurface formation 100. These models can simulate the effects of production field operations (e.g., injecting water or carbon dioxide through the injection well 123 to increase the production of hydrocarbons through the production well 125). The simulations can be used to plan and, in some instances, control field operations (e.g., the operation of pumps 129 associated with the injection well 123 and the production well 125).

[0029] In some embodiments, results generated by the computer systems 124 may be displayed for user viewing using local or remote monitors or other display units. One approach to analyzing seismic data is to associate the data with portions of a seismic cube representing the subsurface formation 100. The seismic cube can also display results of the analysis of the seismic data associated with the seismic survey.

[0030] FIG. 2 is a schematic illustrating an example system 200 for producing natural gas from subsurface formations while altering gas composition in-situ during production. The system 200 includes a control system 210 with a model 212 and a pump control module 214. The control system 210 is in communication a database 216, one or more injection wells 123, and one or more production wells 125. The control system 210 and the database 216 can be implemented on computer systems in a control center as described with reference to FIG. 1.

[0031] The database 216 can be used to store reservoir and operational data generated, for example, by the hydrocarbon exploration and production activities described with reference to FIG. 1. The database 216 is also in communication with the one or more injection wells 123 and the one or more production wells 125. The database 216 receives data about conditions in the subsurface formation from the sensors associated with the wells (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter). The database 216 also receives operational data from the pumps associated with the wells.

[0032] The model 212 is in communication with the pump control module 214. The model 212 predicts the required gas injection volume to alter the subsurface gas composition based on real-time pressure, volume, temperature from downhole samples and well logs to predict injection volumes that will provide keep produced gas high quality. In this implementation, the model 212 is a machine learning model. Based on the injection volumes predicted by the model 212, the pump control module sends signals to the wells 123, 125 controlling the operation of associated pumps.

[0033] FIG. 3 illustrates an implementation of the system 200. In this implementation, the system 200 is configured for producing gas from a subsurface formation through a production well 125. The system 150 is illustrated with an injection well 123, a production well 125, and an observation well 152 drilled into a gas-bearing reservoir 254. Non-gas bearing layers 256 are present above and below the gas-bearing reservoir. The sensors 127 and pumps 129 associated with the wells are in communication with the control center 122 and the associated control systems 124.

[0034] As described with respect to FIG. 2, the system 200 predicts the required gas injection volume to alter the subsurface gas composition in the gas-bearing reservoir 254 to help produce a defined gas composition from the production well 125 to ameliorate the issues associated high H2S concentrations found in the formation before adjustment. In some implementations, the system 200 uses real-time pressure, volume, temperature (PVT) from downhole samples and well logs with a machine learning model to predict injection volumes that will adjust the gas composition being produced from the gas-bearing reservoir 254. For example, this approach can start by adding a injection well and an associated observation well to a field that already includes production wells 125. The observation and injection wells can be equipped with sensors (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) to monitor the current downhole conditions downhole for the training of the machine learning model. The gas chromatograph analyzes the gas sample to determine its composition, while the pressure gauge and temperature gauge provide the pressure and temperature data to be used for PVT analysis. The fluid density meter is used to determine the density of the injected fluid.

[0035] Although illustrated with one injection well 123, one production well, 125, and one observation well 152, such systems will typically have multiple wells of each type. Some implementations do not include observation wells 252 and only rely on sensors in the production wells 125 and / or injection wells 123.

[0036] Implementations of system 200 can also be configured for enhancing CO2 sequestration. These implementations do not require production wells 125 if gas is not being extracted from the reservoir. In such implementations, the system uses real-time pressure, volume, temperature (PVT) from downhole samples and well logs with a machine learning model to predict injection volumes that will enhance a CO2 sequestration process. For example, this approach can start by adding a injection well for sequestration and an associated observation well to a field. The observation and injection wells can be equipped with sensors (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) to monitor the current downhole conditions downhole for the training of the machine learning model. The gas chromatograph analyzes the gas sample to determine its composition, while the pressure gauge and temperature gauge provide the pressure and temperature data to be used for PVT analysis. The fluid density meter is used to determine the density of the injected fluid.

[0037] FIG. 4 is a flow chart illustrating a method 300 of producing natural gas from subsurface formations while altering gas composition in-situ during production. Initially a target reservoir is identified. For example, the reservoir can be a reservoir in which gas composition alteration is needed due to factors such as high concentrations of H2S or other undesired components (e.g., CO2 or, in some cases, water vapor). After the reservoir is identified, monitoring equipment is installed in at least two wells, including a production well and an injection well. The equipment typically include sensors to monitor PVT properties such as pressure, temperature, and composition. The data from these sensors will be used to train the machine learning model. Data from the monitoring equipment, including real-time PVT measurements and well logs, is collected and processed.

[0038] The pressure, temperature, and composition of fluids in the subsurface formation are monitored using sensors installed downhole in an injection well or wells (step 310). In some implementations, the pressure, the temperature, and the composition of fluids in the subsurface formation are continuously monitored. Similarly, the pressure, temperature, and composition of fluids in the subsurface formation are monitored using sensors installed downhole in an production well or wells (step 312) and, in some implementations, are monitored continuously.

[0039] A volume of gas injection required to alter the composition of fluids in the subsurface formation to provide a specific fluid composition in the production well is predicted without use of a simulation model (step 314). In some implementations, a machine learning model is trained using the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an injection well and installed downhole in the production well to identify the impact of changes in injection strategies. Predicting the volume of gas injection using the machine learning model is more computationally efficient than using a simulation model.

[0040] The predicted volume of gas is injected through the injection well(s) using pump(s) associated with the injection well(s) (step 316). For example, injecting the predicted volume of gas can include continuously monitor the composition of the fluids downhole and adjusting injection volume and frequency based on the machine learning model. Gases injected can include CO2, ethane, and nitrogen (N2). If the alteration is not as effective as expected or the PVT is not changing after some injection, additional monitoring can be carried out to determine the cause. This can help to identify any issues with the wellbore or the reservoir and determine if additional actions need to be taken. For example, issues that might arise include: wellbore / surface leakage, formation plugging with a decline in injectivity, and inadequate data. The machine learning model can be trained to classify these issues for further review and / or suggest remediation (e.g., additional pumps), or suggest a retraining of the model. If the PVT is not changing as expected, adjustments can be made to the injection parameters such as volume and frequency of injection. This can help to optimize the injection process and achieve the desired PVT change. In conjunction with the injection, fluids in the subsurface formation are produced to the surface (step 318).

[0041] It is anticipated that the monitoring and modeling for a reservoir will initially be based on a pair of wells. Typically, additional injection and production wells will be incorporated over time. More monitoring equipment to other wells can be added incrementally in the field to monitor the change in composition across the field. This will help to adjust the machine learning model on the amount to inject per well or area. As more wells are drilled, monitoring equipment will be added in these wells to collect data and adjust the machine learning model accordingly.

[0042] FIG. 5 is a diagram illustrating an example computer system 400 configured to execute a machine learning model. Generally, the computer system 400 is configured to process data indicating downhole conditions in a reservoir. The system 400 includes computer processors 410. The computer processors 410 include computer-readable memory 411 and computer readable instructions 412. The system 400 also includes a machine learning system 450. The machine learning system 450 includes a machine learning model 420. The machine learning model 420 can be separate from or integrated with the computer processors 410.

[0043] The computer-readable medium 411 (or computer-readable memory) can include any data storage technology type which is suitable to the local technical environment, including but not limited to semiconductor based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, removable memory, disc memory, flash memory, dynamic random-access memory (DRAM), static random-access memory (SRAM), electronically erasable programmable read-only memory (EEPROM) and the like. In an embodiment, the computer-readable medium 411 includes code-segment having executable instructions.

[0044] In some implementations, the computer processors 410 include a general purpose processor. In some implementations, the computer processors 410 include a central processing unit (CPU). In some implementations, the computer processors 410 include at least one application specific integrated circuit (ASIC). The computer processors 410 can also include general purpose programmable microprocessors, graphic processing units, special-purpose programmable microprocessors, digital signal processors (DSPs), programmable logic arrays (PLAs), field programmable gate arrays (FPGA), special purpose electronic circuits, etc., or a combination thereof. The computer processors 410 are configured to execute program code means such as the computer-executable instructions 412 and configured to execute executable logic that includes the machine learning model 420.

[0045] The computer processors 410 are configured to receive data including: data about conditions in the subsurface formation from the sensors associated with the wells (e.g., a gas chromatograph, a downhole pressure gauge, a downhole temperature gauge, and a fluid density meter) as well as operational data from the pumps associated with the wells. The machine learning model 420 is capable of processing the data to predict the volume of gas injection required to alter the subsurface gas composition to a desired level, while minimizing the use of simulation models.

[0046] The system relies on accurate predictions of gas composition and subsurface conditions. However, unexpected changes in the subsurface (e.g., variations in rock properties or gas flow) sometimes occur and can affect the accuracy of the predictions. Outlier prediction models can be used to identify if the predicted valued started to become significantly different than the previous data. This will help in flagging the data for review of the data received or model's accuracy.

[0047] The machine learning system 450 is capable of applying machine learning techniques to train the machine learning model 420. As part of the training of the machine learning model 420, the machine learning system 450 forms a training set of input data by identifying a positive training set of input data items that have been determined to have the property in question, and, in some embodiments, forms a negative training set of input data items that lack the property in question.

[0048] The machine learning system 450 extracts feature values from the input data of the training set, the features being variables deemed potentially relevant to whether or not the input data items have the associated property or properties. An ordered list of the features for the input data is herein referred to as the feature vector for the input data. In one embodiment, the machine learning system 450 applies dimensionality reduction (e.g., via linear discriminant analysis (LDA), principle component analysis (PCA), or the like) to reduce the amount of data in the feature vectors for the input data to a smaller, more representative set of data.

[0049] In some implementations, the machine learning system 450 uses supervised machine learning to train the machine learning models 420 with the feature vectors of the positive training set and the negative training set serving as the inputs. Different machine learning techniques-such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naïve Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps—may be used in different embodiments. The machine learning model 420, when applied to the feature vector extracted from the input data item, outputs an indication of whether the input data item has the property in question, such as a Boolean yes / no estimate, or a scalar value representing a probability.

[0050] In some embodiments, a validation set is formed of additional input data, other than those in the training sets, which have already been determined to have or to lack the property in question. The machine learning system 450 applies the trained machine learning model 420 to the data of the validation set to quantify the accuracy of the machine learning model 420. Common metrics applied in accuracy measurement include: Precision=TP / (TP+FP) and Recall=TP / (TP+FN), where precision is how many the machine learning model correctly predicted (TP or true positives) out of the total it predicted (TP+FP or false positives), and recall is how many the machine learning model correctly predicted (TP) out of the total number of input data items that did have the property in question (TP+FN or false negatives). The F score (F-score=2*PR / (P+R)) unifies precision and recall into a single measure. In one embodiment, the machine learning module iteratively re-trains the machine learning model until the occurrence of a stopping condition, such as the accuracy measurement indication that the model is sufficiently accurate, or a number of training rounds having taken place.

[0051] In some implementations, the machine learning model 420 is a convolutional neural network (CNN). A CNN can be configured based on a presumption that inputs to the CNN correspond to image pixel data for an image or other data that includes features at multiple spatial locations. For example, sets of inputs can form a multi-dimensional data structure, such as a tensor, that represent color features of an example digital image (e.g., a biological image of biological tissue). In some implementations, inputs to the CNN correspond to a variety of other types of data, such as data obtained from different devices and sensors of a vehicle, point cloud data, audio data that includes certain features or raw audio at each of multiple time steps, or various types of one-dimensional or multiple dimensional data. A convolutional layer of the CNN can process the inputs to transform features of the image that are represented by inputs of the data structure. For example, the inputs are processed by performing dot product operations using input data along a given dimension of the data structure and a set of parameters for the convolutional layer.

[0052] Performing computations for a convolutional layer can include applying one or more sets of kernels to portions of inputs in the data structure. The manner in which CNN performs the computations can be based on specific properties for each layer of an example multi-layer neural network or deep neural network that supports deep neural net workloads. A deep neural network can include one or more convolutional towers (or layers) along with other computational layers. In particular, for example computer vision applications, these convolutional towers often account for a large proportion of the inference calculations that are performed. Convolutional layers of a CNN can have sets of artificial neurons that are arranged in three dimensions, a width dimension, a height dimension, and a depth dimension. The depth dimension corresponds to a third dimension of an input or activation volume and can represent respective color channels of an image. For example, input images can form an input volume of data (e.g., activations), and the volume has dimensions 32×32×3 (width, height, depth respectively). A depth dimension of 3 can correspond to the RGB color channels of red (R), green (G), and blue (B).

[0053] In general, layers of a CNN are configured to transform the three dimensional input volume (inputs) to a multi-dimensional output volume of neuron activations (activations). For example, a 3D input structure of 32×32×3 holds the raw pixel values of an example image, in this case an image of width 32, height 32, and with three color channels, R,G,B. A convolutional layer of a CNN of the machine learning model 420 computes the output of neurons that may be connected to local regions in the input volume. Each neuron in the convolutional layer can be connected only to a local region in the input volume spatially, but to the full depth (e.g., all color channels) of the input volume. For a set of neurons at the convolutional layer, the layer computes a dot product between the parameters (weights) for the neurons and a certain region in the input volume to which the neurons are connected. This computation may result in a volume such as 32×32×12, where 12 corresponds to a number of kernels that are used for the computation. A neuron's connection to inputs of a region can have a spatial extent along the depth axis that is equal to the depth of the input volume. The spatial extent corresponds to spatial dimensions (e.g., x and y dimensions) of a kernel.

[0054] A set of kernels can have spatial characteristics that include a width and a height and that extends through a depth of the input volume. Each set of kernels for the layer is applied to one or more sets of inputs provided to the layer. That is, for each kernel or set of kernels, the machine learning model 420 can overlay the kernel, which can be represented multi-dimensionally, over a first portion of layer inputs (e.g., that form an input volume or input tensor), which can be represented multi-dimensionally. For example, a set of kernels for a first layer of a CNN may have size 5×5×3×16, corresponding to a width of 5 pixels, a height of 5 pixel, a depth of 3 that corresponds to the color channels of the input volume to which to a kernel is being applied, and an output dimension of 16 that corresponds to a number of output channels. In this context, the set of kernels includes 16 kernels so that an output of the convolution has a depth dimension of 16.

[0055] The machine learning model 420 can then compute a dot product from the overlapped elements. For example, the machine learning model 420 can convolve (or slide) each kernel across the width and height of the input volume and compute dot products between the entries of the kernel and inputs for a position or region of the image. Each output value in a convolution output is the result of a dot product between a kernel and some set of inputs from an example input tensor. The dot product can result in a convolution output that corresponds to a single layer input, e.g., an activation element that has an upper-left position in the overlapped multi-dimensional space. As discussed above, a neuron of a convolutional layer can be connected to a region of the input volume that includes multiple inputs. The machine learning model 420 can convolve each kernel over each input of an input volume. The machine learning model 420 can perform this convolution operation by, for example, moving (or sliding) each kernel over each input in the region.

[0056] The machine learning model 420 can move each kernel over inputs of the region based on a stride value for a given convolutional layer. For example, when the stride is set to 1, then the machine learning model 420 can move the kernels over the region one pixel (or input) at a time. Likewise, when the stride is 2, then the machine learning model 420 can move the kernels over the region two pixels at a time. Thus, kernels may be shifted based on a stride value for a layer and the machine learning model 420 can repeatedly perform this process until inputs for the region have a corresponding dot product. Related to the stride value is a skip value. The skip value can identify one or more sets of inputs (2×2), in a region of the input volume, that are skipped when inputs are loaded for processing at a neural network layer. In some implementations, an input volume of pixels for an image can be “padded” with zeros, e.g., around a border region of an image. This zero-padding is used to control the spatial size of the output volumes.

[0057] As discussed previously, a convolutional layer of CNN is configured to transform a three dimensional input volume (inputs of the region) to a multi-dimensional output volume of neuron activations. For example, as the kernel is convolved over the width and height of the input volume, the machine learning model 420 can produce a multi-dimensional activation map that includes results of convolving the kernel at one or more spatial positions based on the stride value. In some cases, increasing the stride value produces smaller output volumes of activations spatially. In some implementations, an activation can be applied to outputs of the convolution before the outputs are sent to a subsequent layer of the CNN.

[0058] An example convolutional layer can have one or more control parameters for the layer that represent properties of the layer. For example, the control parameters can include a number of kernels, K, the spatial extent of the kernels, F, the stride (or skip), S, and the amount of zero padding, P. Numerical values for these parameters, the inputs to the layer, and the parameter values of the kernel for the layer shape the computations that occur at the layer and the size of the output volume for the layer. In some implementations, the spatial size of the output volume is computed as a function of the input volume size, W, using the formula (W?F+2P) / S+1. For example, an input tensor can represent a pixel input volume of size [227×227×3]. A convolutional layer of a CNN can have a spatial extent value of F=11, a stride value of S=4, and no zero-padding (P=0). Using the above formula and a layer kernel quantity of K=96, the machine learning model 420 performs computations for the layer that results in a convolutional layer output volume of size [55×55×96], where 55 is obtained from [(227−11+0) / 4+1=55].

[0059] The computations (e.g., dot product computations) for a convolutional layer, or other layers, of a CNN involve performing mathematical operations, e.g., multiplication and addition, using a computation unit of a hardware circuit of the machine learning model 420. The design of a hardware circuit can cause a system to be limited in its ability to fully utilize computing cells of the circuit when performing computations for layers of a neural network.

[0060] FIG. 6 illustrates hydrocarbon production operations 500 that include both one or more field operations 510 and one or more computational operations 512, which exchange information and control exploration for the production of hydrocarbons. In some implementations, outputs of techniques of the present disclosure can be performed before, during, or in combination with the hydrocarbon production operations 500, specifically, for example, either as field operations 510 or computational operations 512, or both.

[0061] Examples of field operations 510 include forming / drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 510. For example, the methods of the present disclosure can generate data from hardware / software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware / software to the field operations 510 and responsively triggering the field operations 510 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 510. Alternatively or in addition, the field operations 510 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 510 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.

[0062] Examples of computational operations 512 include one or more computer systems 520 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operations 512 can be implemented using one or more databases 518, which store data received from the field operations 510 and / or generated internally within the computational operations 512 (e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systems 520 process inputs from the field operations 510 to assess conditions in the physical world, the outputs of which are stored in the databases 518. For example, seismic sensors of the field operations 510 can be used to perform a seismic survey to map subsurface features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operations 512 where they are stored in the databases 518 and analyzed by the one or more computer systems 520.

[0063] In some implementations, one or more outputs 522 generated by the one or more computer systems 520 can be provided as feedback / input to the field operations 510 (either as direct input or stored in the databases 518). The field operations 510 can use the feedback / input to control physical components used to perform the field operations 510 in the real world.

[0064] For example, the computational operations 512 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 512 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 512 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.

[0065] The one or more computer systems 520 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 512 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 512 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 512 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.

[0066] In some implementations of the computational operations 512, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.

[0067] The presented information can include feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and / or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, when implemented by the user, can improve the speed and accuracy of calculations, strea4ine processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction.

[0068] In some implementations, the feedback can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second(s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.

[0069] Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production / drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and / or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.

[0070] FIG. 7 is a block diagram of an example data processing system 600 used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures described in the present disclosure. For example, the data processing system 600 can be configured predicting the volume of gas injection required to alter the subsurface gas composition to a desired level, while minimizing the use of simulation models. The data processing device 602 can include input devices such as keypads, keyboards, and touch screens that can accept user information. Also, the data processing device 602 can include output devices that can convey information associated with the operation of the data processing device 602. The information can include digital data, visual data, audio information, or a combination of information. The information can be presented in a graphical user interface (UI) (or GUI).

[0071] The data processing device 602 can serve in a role as a client, a network component, a server, a database, a persistency, or components of a computer system for performing the subject matter described in the present disclosure. The illustrated data processing device 602 is communicably coupled with a network 624. In some implementations, one or more components of the data processing device 602 can be configured to operate within different environments, including cloud-computing-based environments, local environments, global environments, and combinations of environments.

[0072] The data processing device 602 can receive requests over network 624 from a client application (for example, executing on another data processing device 602). The data processing device 602 can respond to the received requests by processing the received requests using software applications. Requests can also be sent to the data processing device 602 from internal users (for example, from a command console), external (or third) parties, automated applications, entities, individuals, systems, and computers.

[0073] Each of the components of the data processing device 602 can communicate using a system bus 604. In some implementations, any or all of the components of the data processing device 602, including hardware or software components, can interface with each other or the interface 606 (or a combination of both), over the system bus 604. Interfaces can use an application programming interface (API) 614, a service layer 616, or a combination of the API 614 and service layer 616. The API 614 can include specifications for routines, data structures, and object classes. The API 614 can be either computer-language independent or dependent. The API 614 can refer to a complete interface, a single function, or a set of APIs.

[0074] The service layer 616 can provide software services to the data processing device 602 and other components (whether illustrated or not) that are communicably coupled to the data processing device 602. The functionality of the data processing device 602 can be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer 616, can provide reusable, defined functionalities through a defined interface. For example, the interface can be software written in JAVA, C++, or a language providing data in extensible markup language (X4) format. While illustrated as an integrated component of the data processing device 602, in alternative implementations, the API 614 or the service layer 616 can be stand-alone components in relation to other components of the data processing device 602 and other components communicably coupled to the data processing device 602. Moreover, any or all parts of the API 614 or the service layer 616 can be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of the present disclosure.

[0075] The data processing device 602 includes an interface 606. Although illustrated as a single interface 606 in FIG. 6, two or more interfaces 606 can be used according to implementations of the data processing device 602 and the described functionality. The interface 606 can be used by the data processing device 602 for communicating with other systems that are connected to the network 624 (whether illustrated or not) in a distributed environment. Generally, the interface 606 can include, or be implemented using, logic encoded in software or hardware (or a combination of software and hardware) operable to communicate with the network 624. More specifically, the interface 606 can include software supporting one or more communication protocols associated with communications. As such, the network 624 or the interface's hardware can be operable to communicate physical signals within and outside of the illustrated data processing device 602.

[0076] The data processing device 602 includes a processor 608. Although illustrated as a single processor 608 in FIG. 6, two or more processors 608 can be used according to implementations of the data processing device 602 and the described functionality. Generally, the processor 608 can execute instructions and can manipulate data to perform the operations of the data processing device 602, including operations using algorithms, methods, functions, processes, flows, and procedures as described in the present disclosure.

[0077] The data processing device 602 also includes a database 620 that can hold data (e.g., data about conditions in the subsurface formation from the sensors associated with the wells) for the data processing device 602 and other components connected to the network 624 (whether illustrated or not). For example, database 620 can be in-memory or a database storing data consistent with the present disclosure. In some implementations, database 620 can be a combination of two or more different database types (for example, hybrid in-memory and conventional databases) according to implementations of the data processing device 602 and the described functionality. While database 620 is illustrated as an internal component of the data processing device 602, in alternative implementations, database 620 can be external to the data processing device 602.

[0078] The data processing device 602 also includes a memory 610 that can hold data for the data processing device 602 or a combination of components connected to the network 624 (whether illustrated or not). In some implementations, memory 610 can be a combination of two or more different types of memory (for example, a combination of semiconductor and magnetic storage) according to implementations of the data processing device 602 and the described functionality. While memory 610 is illustrated as an internal component of the data processing device 602, in alternative implementations, memory 610 can be external to the data processing device 602.

[0079] The application 612 can be an algorithmic software engine providing functionality according to implementations of the data processing device 602 and the described functionality. For example, application 612 can serve as one or more components, modules, or applications.

[0080] The data processing device 602 can also include a power supply 618. The power supply 618 can include a rechargeable or non-rechargeable battery that can be configured to be either user- or non-user-replaceable.

[0081] There can be any number of computers 602 associated with, or external to, a computer system including the data processing device 602, with each data processing device 602 communicating over network 624. Further, the terms “client,”“user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one data processing device 602 and one user can use multiple computers 602.

[0082] Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in / on an artificially generated propagated signal. The example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.

[0083] The terms “data processing apparatus,”“computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based).

[0084] The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

[0085] Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent / non-permanent and volatile / non-volatile memory, media, and memory devices. Computer readable media can include, for example, semiconductor memory devices such as random-access memory (RAM), read only memory (ROM), phase change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal / removable disks.EXAMPLES

[0086] In some implementations, systems and methods for producing gas from a subsurface formation through a production well include: monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well; monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole; predicting, without use of a simulation model, a volume of gas injection through required to alter the composition of fluids in the subsurface formation to provide a specific fluid composition in the production well; injecting the predicted volume of gas through the injection well using pumps associated with the injection well; and producing the fluids in the subsurface formation to surface through a production well.

[0087] In an example implementation combinable with any other example implementation, monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole includes continuously monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole in the production well.

[0088] In an example implementation combinable with any other example implementation, the systems and methods also include identifying a target reservoir in which gas injection improves quality of gas produced through the production well. In some cases, the target reservoir has initial H2S concentrations above 10%.

[0089] In an example implementation combinable with any other example implementation, predicting the volume of gas injection includes training a machine learning model using the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an injection well and installed downhole in the production well. In some cases, predicting the volume of gas injection using the machine learning model is more computationally efficient than using a simulation model. In some cases, the injection well is one of a plurality of injection wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation includes monitoring using sensors installed downhole in the plurality of injection wells. In some cases, the production well is one of a plurality of production wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation includes monitoring using sensors installed downhole in the plurality of production wells. In some cases, monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation includes monitoring using sensors installed downhole in a plurality of observation wells. In some cases, injecting the predicted volume of gas through the injection well using the pumps associated with the injection well includes continuously monitor the composition of the fluids and adjusting injection volume and frequency based on the machine learning model. In some cases, injecting the predicted volume of gas through the injection well includes injecting carbon dioxide (CO2) through the injection well.

[0090] In an example implementation combinable with any other example implementation, the sensors are installed downhole in the production well.

[0091] In an example implementation combinable with any other example implementation, the sensors are installed downhole in one or more observation wells.

[0092] In some implementations, systems and methods for sequestering gas in a subsurface formation through an injection well include: monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well; monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an observation well; predicting, without use of a simulation model, a volume of gas injection that can be absorbed by fluids in the subsurface formation; and injecting the predicted volume of gas through the injection well using pumps associated with the injection well.

[0093] In an example implementation combinable with any other example implementation, monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole includes continuously monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole in the observation well.

[0094] In an example implementation combinable with any other example implementation, predicting the volume of gas injection includes training a machine learning model using the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an injection well and installed downhole in the observation well. In some cases, the injection well is one of a plurality of injection wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation includes monitoring using sensors installed downhole in the plurality of injection wells. In some cases, the observation well is one of a plurality of observation wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation includes monitoring using sensors installed downhole in the plurality of observation wells. In some cases, injecting the predicted volume of gas through the injection well includes injecting carbon dioxide (CO2) through the injection well.

[0095] A number of embodiments of the systems and methods have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of this specification. For example, these systems and methods can be used carbon capture sequestration (CSS), carbon capture, utilization & storage (CCUS), carbon Sequestration & enhanced gas recovery (CSEGR), and waste management. Accordingly, other embodiments are within the scope of the following claims.

Claims

1. A method of producing gas from a subsurface formation through a production well, the method comprising:monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well;monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole;predicting, without use of a simulation model, a volume of gas injection through required to alter the composition of fluids in the subsurface formation to provide a specific fluid composition in the production well;injecting the predicted volume of gas through the injection well using pumps associated with the injection well; andproducing the fluids in the subsurface formation to surface through a production well.

2. The method of claim 1, wherein monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole comprises continuously monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole in the production well.

3. The method of claim 1, further comprising identifying a target reservoir in which gas injection improves quality of gas produced through the production well.

4. The method of claim 3, wherein the target reservoir has hydrogen sulfide (H2S) concentrations above 10%.

5. The method of claim 1, wherein predicting the volume of gas injection comprises training a machine learning model using the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an injection well and installed downhole in the production well.

6. The method of claim 5, wherein predicting the volume of gas injection using the machine learning model is more computationally efficient than using a simulation model.

7. The method of claim 5, wherein the injection well is one of a plurality of injection wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation comprises monitoring using sensors installed downhole in the plurality of injection wells.

8. The method of claim 7, wherein the production well is one of a plurality of production wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation comprises monitoring using sensors installed downhole in the plurality of production wells.

9. The method of claim 7, wherein monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation comprises monitoring using sensors installed downhole in a plurality of observation wells.

10. The method of claim 5, wherein injecting the predicted volume of gas through the injection well using the pumps associated with the injection well comprises continuously monitor the composition of the fluids and adjusting injection volume and frequency based on the machine learning model.

11. The method of claim 5, wherein injecting the predicted volume of gas through the injection well comprises injecting carbon dioxide (CO2) through the injection well.

12. The method of claim 1, wherein the sensors are installed downhole in the production well.

13. The method of claim 1, wherein the sensors are installed downhole in one or more observation wells.

14. A method of sequestering gas in a subsurface formation through an injection well, the method comprising:monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well;monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an observation wellpredicting, without use of a simulation model, a volume of gas injection that can be absorbed by fluids in the subsurface formation; andinjecting the predicted volume of gas through the injection well using pumps associated with the injection well.

15. The method of claim 14, wherein monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole comprises continuously monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation using the sensors installed downhole in the observation well.

16. The method of claim 14, wherein predicting the volume of gas injection comprises training a machine learning model using the pressure, the temperature, and the composition of fluids in the subsurface formation using sensors installed downhole in an injection well and installed downhole in the observation well.

17. The method of claim 16, wherein the injection well is one of a plurality of injection wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation comprises monitoring using sensors installed downhole in the plurality of injection wells.

18. The method of claim 17, wherein the observation well is one of a plurality of observation wells and monitoring the pressure, the temperature, and the composition of fluids in the subsurface formation comprises monitoring using sensors installed downhole in the plurality of observation wells.

19. The method of claim 16, wherein injecting the predicted volume of gas through the injection well comprises injecting carbon dioxide (CO2) through the injection well.

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